Methods and systems for optimizing supplement decisions

ABSTRACT

A system for identifying a longevity element to optimize supplement decisions is disclosed. The system includes a computing device configured to capture an identifier of a first longevity element using a data capturing device. The computing device is configured to receive a longevity inquiry from a remote device generating a longevity inquiry from the identifier, the longevity query identifying the first longevity element. The system retrieves a biological extraction pertaining to a user and identifies a longevity element associated with a user. The system selects an ADME model utilizing a biological extraction. The system generates a machine-learning algorithm utilizing the selected ADME model to input a longevity element associated with a user as an input and output an ADME factor. The system identifies a tolerant longevity element utilizing an ADME factor. A method for identifying a longevity element to optimize supplement decisions is also disclosed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This continuation-in-part application claims the benefit of priority ofU.S. Non-Provisional Patent Application Ser. No. 16/699,407, filed onNov. 29, 2019 and entitled “METHODS AND SYSTEMS FOR OPTIMIZINGSUPPLEMENT DECISIONS”, which is incorporated by reference herein in itsentirety.

FIELD OF THE INVENTION

The present invention generally relates to the field of artificialintelligence. In particular, the present invention is directed tomethods and systems for optimizing supplement decisions.

BACKGROUND

Accurate and ideal selection of supplements can be challenging and oftenrequire a multi-factorial approach. Frequently consumers are unawareabout how supplements may be metabolized and distributed throughouttheir bodies. This can often lead to ill-fitting information that canhave potentially deadly consequences. Currently there is an unmet needto strap consumers with such knowledge.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for identifying a longevity element to optimizesupplement decisions is disclosed. The system includes a computingdevice which is configured to capture an identifier of a first longevityelement using a data capturing device. The computing device is furtherconfigured to generate a longevity inquiry from the identifier; thelongevity query identifies the first longevity element. The system isfurther configured to retrieve a first biological extraction from a userdatabase. The system is further configured to select an ADME model as afunction of the first biological extraction. The system is furtherconfigured to generate a machine-learning algorithm utilizing theselected ADME factor. The system is further configured to determine, asa function of the ADME factor, that the first longevity element is atolerant longevity element.

In an aspect, a method of identifying a longevity element to optimizesupplement decisions is disclosed. The method includes capturing anidentifier of a first longevity element using a data capturing device.The method includes generating a longevity inquiry from the identifier,the longevity query identifies the first longevity element. The methodincludes retrieving a first biological extraction from a user database.The method includes selecting an ADME model as a function of the firstbiological extraction. The method includes generating a machine-learningalgorithm utilizing the selected ADME model that inputs the firstlongevity element and outputs an ADME factor. The method includesdetermining, as a function of the ADME factor, that the first longevityelement is a tolerant longevity element.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of asystem for optimizing supplement decisions;

FIG. 2 is a block diagram illustrating an exemplary embodiment of a userdatabase;

FIG. 3 is a diagrammatic representation of dietary model;

FIG. 4 is a block diagram illustrating an exemplary embodiment of amodel database;

FIG. 5 is a diagrammatic representation of genetic classifier;

FIG. 6 is a diagrammatic representation of machine-learning algorithms;

FIG. 7 is a process flow diagram illustrating an exemplary embodiment ofa method of optimizing supplement decisions;

FIG. 8 is a process flow diagram illustrating an exemplary embodiment ofa method of optimizing supplement decisions; and

FIG. 9 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for identifying a longevity element and optimizingsupplement decisions. In an embodiment, a system may comprise acomputing device. The computing device is configured to capture anidentifier of a first longevity element using a data capturing device.The computing device generates a longevity inquiry from the identifieridentifying the first longevity element. A longevity inquiry may includea question or remark as to the best supplement that a user can consume.A longevity inquiry may include a picture, image, or photograph of aparticular brand of supplement. A computing device utilizes a user'sbiological extraction in combination with a longevity inquiry toidentify a longevity element associated with the user. A longevityelement may include a particular brand of supplement or category ofsupplement. For example, a longevity element may include fish oil, or alongevity element may include a category of supplements such asanti-inflammatories, joint support, or heart health. A computing deviceselects an ADME model utilizing a biological extraction and generates amachine-learning algorithm utilizing the ADME model to input a longevityelement associated with the user as an input and outputs an ADME factor.A computing device identifies a tolerant longevity element utilizing anADME factor. An ADME factor provides insights as to how a user may bestabsorb, distribute, metabolize, and eliminate one or more longevityelements. For example, an ADME factor may indicate that a user hasaltered renal elimination and as such may need to consume a longevityelement that is hepatically eliminated.

Referring now to FIG. 1 , an exemplary embodiment of a system 100 foridentifying a longevity element to optimize supplement decisions isillustrated. System 100 includes a computing device 104. Computingdevice 104 may include any computing device 104 as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Computing device 104 may include,be included in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Computing device 104 may include a singlecomputing device 104 operating independently or may include two or morecomputing device 104 operating in concert, in parallel, sequentially orthe like; two or more computing devices 104 may be included together ina single computing device 104 or in two or more computing devices 104.Computing device 104 may interface or communicate with one or moreadditional devices as described below in further detail via a networkinterface device. Network interface device may be utilized forconnecting computing device 104 to one or more of a variety of networks,and one or more devices. Examples of a network interface device include,but are not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices 104, and any combinations thereof. Anetwork may employ a wired and/or a wireless mode of communication. Ingeneral, any network topology may be used. Information (e.g., data,software etc.) may be communicated to and/or from a computer and/or acomputing device 104. Computing device 104 may include but is notlimited to, for example, a computing device 104 or cluster of computingdevices 104 in a first location and a second computing device 104 orcluster of computing devices 104 in a second location. Computing device104 may include one or more computing devices 104 dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Computing device 104 may distribute one or more computing tasks asdescribed below across a plurality of computing devices 104 of computingdevice 104, which may operate in parallel, in series, redundantly, or inany other manner used for distribution of tasks or memory betweencomputing devices 104. Computing device 104 may be implemented using a“shared nothing” architecture in which data is cached at the worker; inan embodiment, this may enable scalability of system 100 and/orcomputing device 104.

Still referring to FIG. 1 , computing device 104 may be designed and/orconfigured to perform any method, method step, or sequence of methodsteps in any embodiment described in this disclosure, in any order andwith any degree of repetition. For instance, computing device 104 may beconfigured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Computing device 104 mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

With continued reference to FIG. 1 , computing device 104 is configuredto receive a longevity inquiry 108 from a remote device 112. A“longevity inquiry 108” as used in this disclosure, includes any inquirygenerated by a user regarding a supplement or longevity element. inquestA “longevity element” as used in this disclosure, includes anysupplement intended to supplement the diet of a human being and/oranimal. Supplements may include products consumed by a user that containa dietary ingredient. Dietary ingredients may include any vitamin,mineral, nutrient, homeopathic, amino acid, herb, botanical,nutraceutical, enzyme, health food, medical food, and the like.Supplements may contain dietary ingredients sourced from food,synthesized in a laboratory, and/or sourced in combination. Supplementsmay include for example, a multi-vitamin, co-enzyme q10, ubiquinol,resveratrol, probiotics such as Lactobacillus acidophilus,Bifidobacterium bifidum, Saccharomyces boulardii, fish oil, B-Vitamincomplex, Vitamin D, cranberry, products containing combinationingredients, and the like. Supplements may be available in a variety ofdifferent dosage forms for a user to consume including for example,capsules, tablets, pills, buccal tablets, sub-lingual tablets,orally-disintegrating products, thin films, liquid solution, liquidsuspension, oil suspension, powder, solid crystals, seeds, foods,pastes, buccal films, inhaled forms such as aerosols, nebulizers, smokedforms, vaporized form, intradermal forms, subcutaneous forms,intramuscular forms, intraosseous forms, intraperitoneal forms,intravenous forms, creams, gels, balms, lotion, ointment, ear drops, eyedrops, skin patch, transdermal forms, vaginal rings, dermal patch,vaginal suppository, rectal suppository, urethral suppository, nasalsuppository, and the like. Supplements may be available to a userwithout a prescription such as for example, a fish oil supplement soldat a health food store. In an embodiment, supplements may be selectedand/or identified after identifying a user's food supply, where a foodsupply includes any food and/or beverage consumed by a human being.Supplements may be available to a user with a prescription, such as forexample subcutaneous cyanocobalamin injections available at acompounding pharmacy. Supplements may be categorized into differentgrade products such as for example pharmaceutical grade supplements thatmay contain in excess of 99% purity and do not contain binders, fillers,excipients, dyes, or unknown substances and are manufactured in Food andDrug Administration (FDA) registered facilities that follow certifiedgood manufacturing practices (cGMP); supplements may be of food gradequality such as for example supplements deemed to be suitable for humanconsumption; supplements may be of feed grade quality such as forexample supplements deemed to be suitable for animal consumption. Alongevity element may include a particular supplement product such as aparticular brand name supplement. A longevity element may include aparticular category of supplement such as supplements to support hearthealth, immune health, women's health, joint health and the like. Alongevity element may include a generic supplement not necessarilyassociated with any particular brand name such as a fish oil supplement,coenzyme q10 supplement, and the like.

With continued reference to FIG. 1 , an inquest in regard to a longevityelement may include one or more questions, problems, issues, and/orinquiries regarding a longevity element. A user may pose a generalquestion about what particular supplements the user should be taking. Auser may pose a question about a particular supplement such as how muchVitamin D a user should be taking. A user may describe one or moresymptoms that the user may be experiencing to inquire about one or moresupplements that may diminish, reduce, and/or eliminate one or moresymptoms. For example, a user may describe symptoms such as a headache,runny nose, fatigue, and a productive cough and generate a longevityinquiry 108 describing such symptoms and ask what supplements may reduceand/or diminish such symptoms. A user may generate a longevity inquiry108 based on an encounter with one or more informed advisors. Aninformed advisor may include any medical professional who may assistand/or participate in the medical treatment of a user. An informedadvisor may include a medical doctor, nurse, physician assistant,pharmacist, yoga instructor, nutritionist, spiritual healer, meditationteacher, fitness coach, health coach, life coach, and the like. Forinstance, and without limitation, an informed advisor such as a user'snutritionist may recommend that user consume a Vitamin B12 supplementbecause user has recently started a vegetarian diet. In such aninstance, user may generate a longevity inquiry 108 to inquire aboutwhat Vitamin B12 supplement user should take, what form of Vitamin B12user should consume such as cyanocobalamin versus hydroxocobalamin andmethylcobalamin, how frequently the user should consume the Vitamin B12,and what dosage form of Vitamin B12 user should consume.

With continued reference to FIG. 1 , computing device 104 receives alongevity inquiry 108 from a remote device 112. Remote device 112 mayinclude without limitation, a display in communication with computingdevice 104, where a display may include any display as described herein.Remote device 112 may include an additional computing device, such as amobile device, laptop, desktop, computer and the like. Remote device 112may transmit and/or receive one or more inputs from computing device 104utilizing any network methodology as described herein. Remote device 112may be operated by a user which may include any human subject. Remotedevice 112 may be operated by an informed advisor. Remote device 112 maybe operated by a family member or friend of a user. For instance andwithout limitation, remote device 112 may be operated by an informedadvisor such as user's functional medicine doctor who may generate alongevity inquiry 108 to determine the best form of Vitamin C for theuser to consume and supplement user's diet with due to a recentdiagnosis of adrenal fatigue. Longevity inquiry 108 may be transmittedfrom a remote device 112 to computing device 104 utilizing any networkmethodology as described herein.

With continued reference to FIG. 1 , computing device 104 may include agraphical user interface 116. Graphical user interface 116 may includewithout limitation a form or other graphical element having data entryfields, wherein a user may select one or more fields to enter one ormore longevity inquires. Graphical user interface 116 may provide adrop-down menu where a user may select a particular longevity element.For instance and without limitation, graphical user interface 116 mayprovide a drop-down menu of particular categories of longevity elementsthat a user may select from such as multi-vitamins, bone health, jointhealth, vision health, heart health, women's health, men's health,gastrointestinal health, healthy blood glucose, immune health,neurological health, stress management, metabolic detoxification, sportsnutrition, children's health and the like. In an embodiment, a user mayselect a category of longevity elements such as heart health andgenerate a longevity inquiry 108 based on the selected category.Graphical user interface 116 may display one or more photographs of oneor more longevity elements that a user may select and generate alongevity inquiry 108 to determine if the user should consume theselected longevity element or if the particular selected longevityelement is compatible with user's body. Graphical user interface 116 mayprovide free form textual entry fields where a user may enter one ormore longevity inquiries. For example, a user may type into a free formtextual entry field an inquiry regarding a longevity element. Graphicaluser interface 116 may also be utilized to display one or more outputsto a user such as a tolerant longevity element as described in moredetail below.

With continued reference to FIG. 1 , computing device 104 is configuredto receive at an image device 120 located on computing device 104 awireless transmission from a remote device 112 containing a photographof a longevity element. An “image device 120” as used in thisdisclosure, includes any device suitable to take a picture and/orphotograph of a longevity element. Image device 120 may include forexample, a camera, mobile phone camera, scanner, a data capturingdevice, or the like. Image device 120 may be located on remote device112. In an embodiment, the computing device may be configured to capturean image identifying the first longevity element. For example, a usermay take a photograph of a longevity element using a data capturingdevice on remote device 112. In an embodiment, the computing device isconfigured to capture an image containing the first longevity element.Image device 120 may be utilized to receive a photograph and/or pictureof a longevity element that contains a unique identifier of thelongevity element.

With continued reference to FIG. 1 , a computing device 104 isconfigured to capture an identifier of a first longevity element using adata capture device. An identifier, as used in this disclosure, includesany data that may be used to describe the longevity element. Anidentifier may include a specific sequence of characters, numbers,letters, and/or words that may identify a particular longevity element.An identifier may include a globally recognized uniform optical codesuch as a uniform code commission (UCC) barcode. An optical code mayinclude a universal product code (“UPC”), a stock keeping code (“SKU”),a quick read code (“QR”), a European article number (“EAN”), or thelike. In an embodiment, the computing device may be configured tocapture the identifier by scanning an optical code using the datacapturing device. A data capture device, as defined in this disclosure,may include any optical capture device which may include a camera, barcode readers, scanners, radio frequency readers or interrogators,transceivers, or the like. For example, a user may utilize a scannerlocated on image device 120 to capture UCC barcode located on alongevity element while user is at a health food store and submit alongevity inquiry 108 to determine if the user should purchase thelongevity element and if the longevity element is compatible with theuser's body. An identifier may also be included in radio frequency tags.For example, these identifiers may originate from radio frequencyidentification (“RFID”) tags or Near Field communication (“NFC”) tags.Interrogate, as used in this disclosure, describes a process ofretrieving an identifier stored on an RFID tag by sending radio waves tothe tag and converting the waves the tag sends back into data andcaptured by the data capturing device. In an embodiment, the computingdevice may be configured to capture the identifier by interrogating aradio frequency identification tag.

With continued reference to FIG. 1 , computing device 104 is configuredto retrieve a biological extraction from a user database. A “biologicalextraction 124” as used in this disclosure includes at least an elementof user physiological data. As used in this disclosure, “physiologicaldata” is any data indicative of a person's physiological state;physiological state may be evaluated with regard to one or more measuresof health of a person's body, one or more systems within a person's bodysuch as a circulatory system, a digestive system, a nervous system, orthe like, one or more organs within a person's body, and/or any othersubdivision of a person's body useful for diagnostic or prognosticpurposes. For instance, and without limitation, a particular set ofbiomarkers, test results, and/or biochemical information may berecognized in a given medical field as useful for identifying variousdisease conditions or prognoses within a relevant field. As anon-limiting example, and without limitation, physiological datadescribing red blood cells, such as red blood cell count, hemoglobinlevels, hematocrit, mean corpuscular volume, mean corpuscularhemoglobin, and/or mean corpuscular hemoglobin concentration may berecognized as useful for identifying various conditions such asdehydration, high testosterone, nutrient deficiencies, kidneydysfunction, chronic inflammation, anemia, and/or blood loss.

With continued reference to FIG. 1 , physiological state data mayinclude, without limitation, hematological data, such as red blood cellcount, which may include a total number of red blood cells in a person'sblood and/or in a blood sample, hemoglobin levels, hematocritrepresenting a percentage of blood in a person and/or sample that iscomposed of red blood cells, mean corpuscular volume, which may be anestimate of the average red blood cell size, mean corpuscularhemoglobin, which may measure average weight of hemoglobin per red bloodcell, mean corpuscular hemoglobin concentration, which may measure anaverage concentration of hemoglobin in red blood cells, platelet count,mean platelet volume which may measure the average size of platelets,red blood cell distribution width, which measures variation in red bloodcell size, absolute neutrophils, which measures the number of neutrophilwhite blood cells, absolute quantities of lymphocytes such as B-cells,T-cells, Natural Killer Cells, and the like, absolute numbers ofmonocytes including macrophage precursors, absolute numbers ofeosinophils, and/or absolute counts of basophils. Physiological statedata may include, without limitation, immune function data such asInterleukine-6 (IL-6), TNF-alpha, systemic inflammatory cytokines, andthe like.

Continuing to refer to FIG. 1 , physiological state data may include,without limitation, data describing blood-born lipids, including totalcholesterol levels, high-density lipoprotein (HDL) cholesterol levels,low-density lipoprotein (LDL) cholesterol levels, very low-densitylipoprotein (VLDL) cholesterol levels, levels of triglycerides, and/orany other quantity of any blood-born lipid or lipid-containingsubstance. Physiological state data may include measures of glucosemetabolism such as fasting glucose levels and/or hemoglobin A1-C (HbA1c)levels. Physiological state data may include, without limitation, one ormore measures associated with endocrine function, such as withoutlimitation, quantities of dehydroepiandrosterone (DHEAS), DHEA-Sulfate,quantities of cortisol, ratio of DHEAS to cortisol, quantities oftestosterone quantities of estrogen, quantities of growth hormone (GH),insulin-like growth factor 1 (IGF-1), quantities of adipokines such asadiponectin, leptin, and/or ghrelin, quantities of somatostatin,progesterone, or the like. Physiological state data may include measuresof estimated glomerular filtration rate eGFR). Physiological state datamay include quantities of C-reactive protein, estradiol, ferritin,folate, homocysteine, prostate-specific Ag, thyroid-stimulating hormone,vitamin D, 25 hydroxy, blood urea nitrogen, creatinine, sodium,potassium, chloride, carbon dioxide, uric acid, albumin, globulin,calcium, phosphorus, alkaline phosphatase, alanine amino transferase,aspartate amino transferase, lactate dehydrogenase (LDH), bilirubin,gamma-glutamyl transferase (GGT), iron, and/or total iron bindingcapacity (TIBC), or the like. Physiological state data may includeantinuclear antibody levels. Physiological state data may includealuminum levels. Physiological state data may include arsenic levels.Physiological state data may include levels of fibrinogen, plasmacystatin C, and/or brain natriuretic peptide.

Continuing to refer to FIG. 1 , physiological state data may includemeasures of lung function such as forced expiratory volume, one second(FEV-1) which measures how much air can be exhaled in one secondfollowing a deep inhalation, forced vital capacity (FVC), which measuresthe volume of air that may be contained in the lungs. Physiologicalstate data may include a measurement blood pressure, including withoutlimitation systolic and diastolic blood pressure. Physiological statedata may include a measure of waist circumference. Physiological statedata may include body mass index (BMI). Physiological state data mayinclude one or more measures of bone mass and/or density such asdual-energy x-ray absorptiometry. Physiological state data may includeone or more measures of muscle mass. Physiological state data mayinclude one or more measures of physical capability such as withoutlimitation measures of grip strength, evaluations of standing balance,evaluations of gait speed, pegboard tests, timed up and go tests, and/orchair rising tests.

Still viewing FIG. 1 , physiological state data may include one or moremeasures of cognitive function, including without limitation Reyauditory verbal learning test results, California verbal learning testresults, NIH toolbox picture sequence memory test, Digital symbol codingevaluations, and/or Verbal fluency evaluations. Physiological state datamay include one or more evaluations of sensory ability, includingmeasures of audition, vision, olfaction, gustation, vestibular functionand pain.

Continuing to refer to FIG. 1 , physiological state data may includepsychological data. Psychological data may include any data generatedusing psychological, neuro-psychological, and/or cognitive evaluations,as well as diagnostic screening tests, personality tests, personalcompatibility tests, or the like; such data may include, withoutlimitation, numerical score data entered by an evaluating professionaland/or by a subject performing a self-test such as a computerizedquestionnaire. Psychological data may include textual, video, or imagedata describing testing, analysis, and/or conclusions entered by amedical professional such as without limitation a psychologist,psychiatrist, psychotherapist, social worker, a medical doctor, or thelike. Psychological data may include data gathered from userinteractions with persons, documents, and/or computing devices; forinstance, user patterns of purchases, including electronic purchases,communication such as via chat-rooms or the like, any textual, image,video, and/or data produced by the subject, any textual image, videoand/or other data depicting and/or describing the subject, or the like.Any psychological data and/or data used to generate psychological datamay be analyzed using machine-learning and/or language processing module136 as described in this disclosure.

Still referring to FIG. 1 , physiological state data may include genomicdata, including deoxyribonucleic acid (DNA) samples and/or sequences,such as without limitation DNA sequences contained in one or morechromosomes in human cells. Genomic data may include, withoutlimitation, ribonucleic acid (RNA) samples and/or sequences, such assamples and/or sequences of messenger RNA (mRNA) or the like taken fromhuman cells. Genetic data may include telomere lengths. Genomic data mayinclude epigenetic data including data describing one or more states ofmethylation of genetic material. Physiological state data may includeproteomic data, which as used herein is data describing all proteinsproduced and/or modified by an organism, colony of organisms, or systemof organisms, and/or a subset thereof. Physiological state data mayinclude data concerning a microbiome of a person, which as used hereinincludes any data describing any microorganism and/or combination ofmicroorganisms living on or within a person, including withoutlimitation biomarkers, genomic data, proteomic data, and/or any othermetabolic or biochemical data useful for analysis of the effect of suchmicroorganisms on other physiological state data of a person, asdescribed in further detail below.

With continuing reference to FIG. 1 , physiological state data mayinclude one or more user-entered descriptions of a person'sphysiological state. One or more user-entered descriptions may include,without limitation, user descriptions of symptoms, which may includewithout limitation current or past physical, psychological, perceptual,and/or neurological symptoms, user descriptions of current or pastphysical, emotional, and/or psychological problems and/or concerns, userdescriptions of past or current treatments, including therapies,nutritional regimens, exercise regimens, pharmaceuticals or the like, orany other user-entered data that a user may provide to a medicalprofessional when seeking treatment and/or evaluation, and/or inresponse to medical intake papers, questionnaires, questions frommedical professionals, or the like. Physiological state data may includeany physiological state data, as described above, describing anymulticellular organism living in or on a person including any parasiticand/or symbiotic organisms living in or on the persons; non-limitingexamples may include mites, nematodes, flatworms, or the like. Examplesof physiological state data described in this disclosure are presentedfor illustrative purposes only and are not meant to be exhaustive.

With continued reference to FIG. 1 , physiological data may include,without limitation any result of any medical test, physiologicalassessment, cognitive assessment, psychological assessment, or the like.System 100 may receive at least a physiological data from one or moreother devices after performance; system 100 may alternatively oradditionally perform one or more assessments and/or tests to obtain atleast a physiological data, and/or one or more portions thereof, onsystem 100. For instance, at least physiological data may include ormore entries by a user in a form or similar graphical user interface 116object; one or more entries may include, without limitation, userresponses to questions on a psychological, behavioral, personality, orcognitive test. For instance, at least a server 104 may present to usera set of assessment questions designed or intended to evaluate a currentstate of mind of the user, a current psychological state of the user, apersonality trait of the user, or the like; at least a server 104 mayprovide user-entered responses to such questions directly as at least aphysiological data and/or may perform one or more calculations or otheralgorithms to derive a score or other result of an assessment asspecified by one or more testing protocols, such as automatedcalculation of a Stanford-Binet and/or Wechsler scale for IQ testing, apersonality test scoring such as a Myers-Briggs test protocol, or otherassessments that may occur to persons skilled in the art upon reviewingthe entirety of this disclosure.

With continued reference to FIG. 1 , assessment and/or self-assessmentdata, and/or automated or other assessment results, obtained from athird-party device; third-party device may include, without limitation,a server or other device (not shown) that performs automated cognitive,psychological, behavioral, personality, or other assessments.Third-party device may include a device operated by an informed advisor.An informed advisor may include any medical professional who may assistand/or participate in the medical treatment of a user. An informedadvisor may include a medical doctor, nurse, physician assistant,pharmacist, yoga instructor, nutritionist, spiritual healer, meditationteacher, fitness coach, health coach, life coach, and the like.

With continued reference to FIG. 1 , physiological data may include datadescribing one or more test results, including results of mobilitytests, stress tests, dexterity tests, endocrinal tests, genetic tests,and/or electromyographic tests, biopsies, radiological tests, genetictests, and/or sensory tests. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalexamples of at least a physiological sample consistent with thisdisclosure.

With continued reference to FIG. 1 , physiological data may include oneor more user body measurements. A “user body measurement” as used inthis disclosure, includes a measurable indicator of the severity,absence, and/or presence of a disease state. A “disease state” as usedin this disclosure, includes any harmful deviation from the normalstructural and/or function state of a human being. A disease state mayinclude any medical condition and may be associated with specificsymptoms and signs. A disease state may be classified into differenttypes including infectious diseases, deficiency diseases, hereditarydiseases, and/or physiological diseases. For instance and withoutlimitation, internal dysfunction of the immune system may produce avariety of different diseases including immunodeficiency,hypersensitivity, allergies, and/or autoimmune disorders.

With continued reference to FIG. 1 , user body measurements may berelated to particular dimensions of the human body. A “dimension of thehuman body” as used in this disclosure, includes one or more functionalbody systems that are impaired by disease in a human body and/or animalbody. Functional body systems may include one or more body systemsrecognized as attributing to root causes of disease by functionalmedicine practitioners and experts. A “root cause” as used in thisdisclosure, includes any chain of causation describing underlyingreasons for a particular disease state and/or medical condition insteadof focusing solely on symptomatology reversal. Root cause may includechains of causation developed by functional medicine practices that mayfocus on disease causation and reversal. For instance and withoutlimitation, a medical condition such as diabetes may include a chain ofcausation that does not include solely impaired sugar metabolism butthat also includes impaired hormone systems including insulinresistance, high cortisol, less than optimal thyroid production, and lowsex hormones. Diabetes may include further chains of causation thatinclude inflammation, poor diet, delayed food allergies, leaky gut,oxidative stress, damage to cell membranes, and dysbiosis. Dimensions ofthe human body may include but are not limited to epigenetics, gut-wall,microbiome, nutrients, genetics, and/or metabolism.

With continued reference to FIG. 1 , epigenetic, as used herein,includes any user body measurements describing changes to a genome thatdo not involve corresponding changes in nucleotide sequence. Epigeneticbody measurement may include data describing any heritable phenotypic.Phenotype, as used herein, include any observable trait of a userincluding morphology, physical form, and structure. Phenotype mayinclude a user's biochemical and physiological properties, behavior, andproducts of behavior. Behavioral phenotypes may include cognitive,personality, and behavior patterns. This may include effects on cellularand physiological phenotypic traits that may occur due to external orenvironmental factors. For example, DNA methylation and histonemodification may alter phenotypic expression of genes without alteringunderlying DNA sequence. Epigenetic body measurements may include datadescribing one or more states of methylation of genetic material.

With continued reference to FIG. 1 , gut-wall, as used herein, includesthe space surrounding the lumen of the gastrointestinal tract that iscomposed of four layers including the mucosa, submucosa, muscular layer,and serosa. The mucosa contains the gut epithelium that is composed ofgoblet cells that function to secrete mucus, which aids in lubricatingthe passage of food throughout the digestive tract. The goblet cellsalso aid in protecting the intestinal wall from destruction by digestiveenzymes. The mucosa includes villi or folds of the mucosa located in thesmall intestine that increase the surface area of the intestine. Thevilli contain a lacteal, that is a vessel connected to the lymph systemthat aids in removal of lipids and tissue fluids. Villi may containmicrovilli that increase the surface area over which absorption can takeplace. The large intestine lack villi and instead a flat surfacecontaining goblet cells are present.

With continued reference to FIG. 1 , gut-wall includes the submucosa,which contains nerves, blood vessels, and elastic fibers containingcollagen. Elastic fibers contained within the submucosa aid instretching the gastrointestinal tract with increased capacity while alsomaintaining the shape of the intestine. Gut-wall includes muscular layerwhich contains smooth muscle that aids in peristalsis and the movementof digested material out of and along the gut. Gut-wall includes theserosa which is composed of connective tissue and coated in mucus toprevent friction damage from the intestine rubbing against other tissue.Mesenteries are also found in the serosa and suspend the intestine inthe abdominal cavity to stop it from being disturbed when a person isphysically active.

With continued reference to FIG. 1 , gut-wall body measurement mayinclude data describing one or more test results including results ofgut-wall function, gut-wall integrity, gut-wall strength, gut-wallabsorption, gut-wall permeability, intestinal absorption, gut-wallbarrier function, gut-wall absorption of bacteria, gut-wallmalabsorption, gut-wall gastrointestinal imbalances and the like.

With continued reference to FIG. 1 , gut-wall body measurement mayinclude any data describing blood test results of creatinine levels,lactulose levels, zonulin levels, and mannitol levels. Gut-wall bodymeasurement may include blood test results of specific gut-wall bodymeasurements including d-lactate, endotoxin lipopolysaccharide (LPS)Gut-wall body measurement may include data breath tests measuringlactulose, hydrogen, methane, lactose, and the like. Gut-wall bodymeasurement may include blood test results describing blood chemistrylevels of albumin, bilirubin, complete blood count, electrolytes,minerals, sodium, potassium, calcium, glucose, blood clotting factors,

With continued reference to FIG. 1 , gut-wall body measurement mayinclude one or more stool test results describing presence or absence ofparasites, firmicutes, Bacteroidetes, absorption, inflammation, foodsensitivities. Stool test results may describe presence, absence, and/ormeasurement of acetate, aerobic bacterial cultures, anerobic bacterialcultures, fecal short chain fatty acids, beta-glucuronidase,cholesterol, chymotrypsin, fecal color, cryptosporidium EIA, Entamoebahistolytica, fecal lactoferrin, Giardia lamblia EIA, long chain fattyacids, meat fibers and vegetable fibers, mucus, occult blood, parasiteidentification, phospholipids, propionate, putrefactive short chainfatty acids, total fecal fat, triglycerides, yeast culture, n-butyrate,pH and the like.

With continued reference to FIG. 1 , gut-wall body measurement mayinclude one or more stool test results describing presence, absence,and/or measurement of microorganisms including bacteria, archaea, fungi,protozoa, algae, viruses, parasites, worms, and the like. Stool testresults may contain species such as Bifidobacterium species,Campylobacter species, Clostridium difficile, Cryptosporidium species,Cyclospora cayetanensis, Cryptosporidium EIA, Dientamoeba fragilis,Entamoeba histolytica, Escherichia coli, Entamoeba histolytica, Giardia,H. pylori, Candida albicans, Lactobacillus species, worms, macroscopicworms, mycology, protozoa, Shiga toxin E. coli, and the like.

With continued reference to FIG. 1 , gut-wall body measurement mayinclude one or more microscopic ova exam results, microscopic parasiteexam results, protozoan polymerase chain reaction test results and thelike. Gut-wall body measurement may include enzyme-linked immunosorbentassay (ELISA) test results describing immunoglobulin G (Ig G) foodantibody results, immunoglobulin E (Ig E) food antibody results, Ig Emold results, IgG spice and herb results. Gut-wall body measurement mayinclude measurements of calprotectin, eosinophil protein x (EPX), stoolweight, pancreatic elastase, total urine volume, blood creatininelevels, blood lactulose levels, blood mannitol levels.

With continued reference to FIG. 1 , gut-wall body measurement mayinclude one or more elements of data describing one or more proceduresexamining gut including for example colonoscopy, endoscopy, large andsmall molecule challenge and subsequent urinary recovery using largemolecules such as lactulose, polyethylene glycol-3350, and smallmolecules such as mannitol, L-rhamnose, polyethyleneglycol-400. Gut-wallbody measurement may include data describing one or more images such asx-ray, Mill, CT scan, ultrasound, standard barium follow-throughexamination, barium enema, barium with contract, Mill fluoroscopy,positron emission tomography 9PET), diffusion-weighted Mill imaging, andthe like.

With continued reference to FIG. 1 , microbiome, as used herein,includes ecological community of commensal, symbiotic, and pathogenicmicroorganisms that reside on or within any of a number of human tissuesand biofluids. For example, human tissues and biofluids may include theskin, mammary glands, placenta, seminal fluid, uterus, vagina, ovarianfollicles, lung, saliva, oral mucosa, conjunctiva, biliary, andgastrointestinal tracts. Microbiome may include for example, bacteria,archaea, protists, fungi, and viruses. Microbiome may include commensalorganisms that exist within a human being without causing harm ordisease. Microbiome may include organisms that are not harmful butrather harm the human when they produce toxic metabolites such astrimethylamine. Microbiome may include pathogenic organisms that causehost damage through virulence factors such as producing toxicby-products. Microbiome may include populations of microbes such asbacteria and yeasts that may inhabit the skin and mucosal surfaces invarious parts of the body. Bacteria may include for example Firmicutesspecies, Bacteroidetes species, Proteobacteria species, Verrumicrobiaspecies, Actinobacteria species, Fusobacteria species, Cyanobacteriaspecies and the like. Archaea may include methanogens such asMethanobrevibacter smithies' and Methanosphaera stadtmanae. Fungi mayinclude Candida species and Malassezia species. Viruses may includebacteriophages. Microbiome species may vary in different locationsthroughout the body. For example, the genitourinary system may contain ahigh prevalence of Lactobacillus species while the gastrointestinaltract may contain a high prevalence of Bifidobacterium species while thelung may contain a high prevalence of Streptococcus and Staphylococcusspecies.

With continued reference to FIG. 1 , microbiome body measurement mayinclude one or more stool test results describing presence, absence,and/or measurement of microorganisms including bacteria, archaea, fungi,protozoa, algae, viruses, parasites, worms, and the like. Stool testresults may contain species such as Ackerman's muciniphila,Anaerotruncus colihominis, bacteriology, Bacteroides vulgates',Bacteroides-Prevotella, Barnesiella species, Bifidobacterium longarm,Bifidobacterium species, Butyrivbrio crossotus, Clostridium species,Collinsella aerofaciens, fecal color, fecal consistency, Coprococcuseutactus, Desulfovibrio piger, Escherichia coli, Faecalibacteriumprausnitzii, Fecal occult blood, Firmicutes to Bacteroidetes ratio,Fusobacterium species, Lactobacillus species, Methanobrevibactersmithii, yeast minimum inhibitory concentration, bacteria minimuminhibitory concentration, yeast mycology, fungi mycology, Odoribacterspecies, Oxalobacter formigenes, parasitology, Prevotella species,Pseudoflavonifractor species, Roseburia species, Ruminococcus species,Veillonella species and the like.

With continued reference to FIG. 1 , microbiome body measurement mayinclude one or more stool tests results that identify all microorganismsliving a user's gut including bacteria, viruses, archaea, yeast, fungi,parasites, and bacteriophages. Microbiome body measurement may includeDNA and RNA sequences from live microorganisms that may impact a user'shealth. Microbiome body measurement may include high resolution of bothspecies and strains of all microorganisms. Microbiome body measurementmay include data describing current microbe activity. Microbiome bodymeasurement may include expression of levels of active microbial genefunctions. Microbiome body measurement may include descriptions ofsources of disease causing microorganisms, such as viruses found in thegastrointestinal tract such as raspberry bushy swarf virus fromconsuming contaminated raspberries or Pepino mosaic virus from consumingcontaminated tomatoes.

With continued reference to FIG. 1 , microbiome body measurement mayinclude one or more blood test results that identify metabolitesproduced by microorganisms. Metabolites may include for example,indole-3-propionic acid, indole-3-lactic acid, indole-3-acetic acid,tryptophan, serotonin, kynurenine, total indoxyl sulfate, tyrosine,xanthine, 3-methylxanthine, uric acid, and the like.

With continued reference to FIG. 1 , microbiome body measurement mayinclude one or more breath test results that identify certain strains ofmicroorganisms that may be present in certain areas of a user's body.This may include for example, lactose intolerance breath tests,methane-based breath tests, hydrogen based breath tests, fructose basedbreath tests. Helicobacter pylori breath test, fructose intolerancebreath test, bacterial overgrowth syndrome breath tests and the like.

With continued reference to FIG. 1 , microbiome body measurement mayinclude one or more urinary analysis results for certain microbialstrains present in urine. This may include for example, urinalysis thatexamines urine specific gravity, urine cytology, urine sodium, urineculture, urinary calcium, urinary hematuria, urinary glucose levels,urinary acidity, urinary protein, urinary nitrites, bilirubin, red bloodcell urinalysis, and the like.

With continued reference to FIG. 1 , nutrient as used herein, includesany substance required by the human body to function. Nutrients mayinclude carbohydrates, protein, lipids, vitamins, minerals,antioxidants, fatty acids, amino acids, and the like. Nutrients mayinclude for example vitamins such as thiamine, riboflavin, niacin,pantothenic acid, pyridoxine, biotin, folate, cobalamin, Vitamin C,Vitamin A, Vitamin D, Vitamin E, and Vitamin K. Nutrients may includefor example minerals such as sodium, chloride, potassium, calcium,phosphorous, magnesium, sulfur, iron, zinc, iodine, selenium, copper,manganese, fluoride, chromium, molybdenum, nickel, aluminum, silicon,vanadium, arsenic, and boron.

With continued reference to FIG. 1 , nutrients may include extracellularnutrients that are free floating in blood and exist outside of cells.Extracellular nutrients may be located in serum. Nutrients may includeintracellular nutrients which may be absorbed by cells including whiteblood cells and red blood cells.

With continued reference to FIG. 1 , nutrient body measurement mayinclude one or more blood test results that identify extracellular andintracellular levels of nutrients. Nutrient body measurement may includeblood test results that identify serum, white blood cell, and red bloodcell levels of nutrients. For example, nutrient body measurement mayinclude serum, white blood cell, and red blood cell levels ofmicronutrients such as Vitamin A, Vitamin B1, Vitamin B2, Vitamin B3,Vitamin B6, Vitamin B12, Vitamin B5, Vitamin C, Vitamin D, Vitamin E,Vitamin K1, Vitamin K2, and folate.

With continued reference to FIG. 1 , nutrient body measurement mayinclude one or more blood test results that identify serum, white bloodcell and red blood cell levels of nutrients such as calcium, manganese,zinc, copper, chromium, iron, magnesium, copper to zinc ratio, choline,inositol, carnitine, methylmalonic acid (MMA), sodium, potassium,asparagine, glutamine, serine, coenzyme q10, cysteine, alpha lipoicacid, glutathione, selenium, eicosapentaenoic acid (EPA),docosahexaenoic acid (DHA), docosapentaenoic acid (DPA), total omega-3,lauric acid, arachidonic acid, oleic acid, total omega 6, and omega 3index.

With continued reference to FIG. 1 , nutrient body measurement mayinclude one or more salivary test results that identify levels ofnutrients including any of the nutrients as described herein. Nutrientbody measurement may include hair analysis of levels of nutrientsincluding any of the nutrients as described herein.

With continued reference to FIG. 1 , genetic as used herein, includesany inherited trait. Inherited traits may include genetic materialcontained with DNA including for example, nucleotides. Nucleotidesinclude adenine (A), cytosine (C), guanine (G), and thymine (T). Geneticinformation may be contained within the specific sequence of anindividual's nucleotides and sequence throughout a gene or DNA chain.Genetics may include how a particular genetic sequence may contribute toa tendency to develop a certain disease such as cancer or Alzheimer'sdisease.

With continued reference to FIG. 1 , genetic body measurement mayinclude one or more results from one or more blood tests, hair tests,skin tests, urine, amniotic fluid, buccal swabs and/or tissue test toidentify a user's particular sequence of nucleotides, genes,chromosomes, and/or proteins. Genetic body measurement may include teststhat example genetic changes that may lead to genetic disorders. Geneticbody measurement may detect genetic changes such as deletion of geneticmaterial or pieces of chromosomes that may cause Duchenne MuscularDystrophy. Genetic body measurement may detect genetic changes such asinsertion of genetic material into DNA or a gene such as the BRCA1genethat is associated with an increased risk of breast and ovarian cancerdue to insertion of 2 extra nucleotides. Genetic body measurement mayinclude a genetic change such as a genetic substitution from a piece ofgenetic material that replaces another as seen with sickle cell anemiawhere one nucleotide is substituted for another. Genetic bodymeasurement may detect a genetic change such as a duplication when extragenetic material is duplicated one or more times within a person'sgenome such as with Charcot-Marie Tooth disease type 1. Genetic bodymeasurement may include a genetic change such as an amplification whenthere is more than a normal number of copies of a gene in a cell such asHER2 amplification in cancer cells. Genetic body measurement may includea genetic change such as a chromosomal translocation when pieces ofchromosomes break off and reattach to another chromosome such as withthe BCR-ABL1 gene sequence that is formed when pieces of chromosome 9and chromosome 22 break off and switch places. Genetic body measurementmay include a genetic change such as an inversion when one chromosomeexperiences two breaks and the middle piece is flipped or invertedbefore reattaching. Genetic body measurement may include a repeat suchas when regions of DNA contain a sequence of nucleotides that repeat anumber of times such as for example in Huntington's disease or Fragile Xsyndrome. Genetic body measurement may include a genetic change such asa trisomy when there are three chromosomes instead of the usual pair asseen with Down syndrome with a trisomy of chromosome 21, Edwardssyndrome with a trisomy at chromosome 18 or Patau syndrome with atrisomy at chromosome 13. Genetic body measurement may include a geneticchange such as monosomy such as when there is an absence of a chromosomeinstead of a pair, such as in Turner syndrome.

With continued reference to FIG. 1 , genetic body measurement mayinclude an analysis of COMT gene that is responsible for producingenzymes that metabolize neurotransmitters. Genetic body measurement mayinclude an analysis of DRD2 gene that produces dopamine receptors in thebrain. Genetic body measurement may include an analysis of ADRA2B genethat produces receptors for noradrenaline. Genetic body measurement mayinclude an analysis of 5-HTTLPR gene that produces receptors forserotonin. Genetic body measurement may include an analysis of BDNF genethat produces brain derived neurotrophic factor. Genetic bodymeasurement may include an analysis of 9p21 gene that is associated withcardiovascular disease risk. Genetic body measurement may include ananalysis of APOE gene that is involved in the transportation of bloodlipids such as cholesterol. Genetic body measurement may include ananalysis of NOS3 gene that is involved in producing enzymes involved inregulating vaso-dilation and vaso-constriction of blood vessels.

With continued reference to FIG. 1 , genetic body measurement mayinclude ACE gene that is involved in producing enzymes that regulateblood pressure. Genetic body measurement may include SLCO1B1 gene thatdirects pharmaceutical compounds such as statins into cells. Geneticbody measurement may include FUT2 gene that produces enzymes that aid inabsorption of Vitamin B12 from digestive tract. Genetic body measurementmay include MTHFR gene that is responsible for producing enzymes thataid in metabolism and utilization of Vitamin B9 or folate. Genetic bodymeasurement may include SHMT1 gene that aids in production andutilization of Vitamin B9 or folate. Genetic body measurement mayinclude MTRR gene that produces enzymes that aid in metabolism andutilization of Vitamin B12. Genetic body measurement may include MTRgene that produces enzymes that aid in metabolism and utilization ofVitamin B12. Genetic body measurement may include FTO gene that aids infeelings of satiety or fullness after eating. Genetic body measurementmay include MC4R gene that aids in producing hunger cues and hungertriggers. Genetic body measurement may include APOA2 gene that directsbody to produce ApoA2 thereby affecting absorption of saturated fats.Genetic body measurement may include UCP1 gene that aids in controllingmetabolic rate and thermoregulation of body. Genetic body measurementmay include TCF7L2 gene that regulates insulin secretion. Genetic bodymeasurement may include AMY1 gene that aids in digestion of starchyfoods. Genetic body measurement may include MCM6 gene that controlsproduction of lactase enzyme that aids in digesting lactose found indairy products. Genetic body measurement may include BCMO1 gene thataids in producing enzymes that aid in metabolism and activation ofVitamin A. Genetic body measurement may include SLC23A1 gene thatproduce and transport Vitamin C. Genetic body measurement may includeCYP2R1 gene that produce enzymes involved in production and activationof Vitamin D. Genetic body measurement may include GC gene that produceand transport Vitamin D. Genetic body measurement may include CYP1A2gene that aid in metabolism and elimination of caffeine. Genetic bodymeasurement may include CYP17A1 gene that produce enzymes that convertprogesterone into androgens such as androstenedione, androstendiol,dehydroepiandrosterone, and testosterone.

With continued reference to FIG. 1 , genetic body measurement mayinclude CYP19A1 gene that produce enzymes that convert androgens such asandrostenedione and testosterone into estrogens including estradiol andestrone. Genetic body measurement may include SRD5A2 gene that aids inproduction of enzymes that convert testosterone intodihydrotestosterone. Genetic body measurement may include UFT2B17 genethat produces enzymes that metabolize testosterone anddihydrotestosterone. Genetic body measurement may include CYP1A1 genethat produces enzymes that metabolize estrogens into 2 hydroxy-estrogen.Genetic body measurement may include CYP1B1 gene that produces enzymesthat metabolize estrogens into 4 hydroxy-estrogen. Genetic bodymeasurement may include CYP3A4 gene that produces enzymes thatmetabolize estrogen into 16 hydroxy-estrogen. Genetic body measurementmay include COMT gene that produces enzymes that metabolize 2hydroxy-estrogen and 4 hydroxy-estrogen into methoxy estrogen. Geneticbody measurement may include GSTT1 gene that produces enzymes thateliminate toxic by-products generated from metabolism of estrogens.Genetic body measurement may include GSTM1 gene that produces enzymesresponsible for eliminating harmful by-products generated frommetabolism of estrogens. Genetic body measurement may include GSTP1 genethat produces enzymes that eliminate harmful by-products generated frommetabolism of estrogens. Genetic body measurement may include SOD2 genethat produces enzymes that eliminate oxidant by-products generated frommetabolism of estrogens.

With continued reference to FIG. 1 , metabolic, as used herein, includesany process that converts food and nutrition into energy. Metabolic mayinclude biochemical processes that occur within the body. Metabolic bodymeasurement may include blood tests, hair tests, skin tests, amnioticfluid, buccal swabs and/or tissue test to identify a user's metabolism.Metabolic body measurement may include blood tests that examine glucoselevels, electrolytes, fluid balance, kidney function, and liverfunction. Metabolic body measurement may include blood tests thatexamine calcium levels, albumin, total protein, chloride levels, sodiumlevels, potassium levels, carbon dioxide levels, bicarbonate levels,blood urea nitrogen, creatinine, alkaline phosphatase, alanine aminotransferase, aspartate amino transferase, bilirubin, and the like.

With continued reference to FIG. 1 , metabolic body measurement mayinclude one or more blood, saliva, hair, urine, skin, and/or buccalswabs that examine levels of hormones within the body such as11-hydroxy-androstereone, 11-hydroxy-etiocholanolone,11-keto-androsterone, 11-keto-etiocholanolone, 16 alpha-hydroxyestrone,2-hydroxyestrone, 4-hydroxyestrone, 4-methoxyestrone, androstanediol,androsterone, creatinine, DHEA, estradiol, estriol, estrone,etiocholanolone, pregnanediol, pregnanestriol, specific gravity,testosterone, tetrahydrocortisol, tetrahydrocrotisone,tetrahydrodeoxycortisol, allo-tetrahydrocortisol.

With continued reference to FIG. 1 , metabolic body measurement mayinclude one or more metabolic rate test results such as breath teststhat may analyze a user's resting metabolic rate or number of caloriesthat a user's body burns each day rest. Metabolic body measurement mayinclude one or more vital signs including blood pressure, breathingrate, pulse rate, temperature, and the like. Metabolic body measurementmay include blood tests such as a lipid panel such as low densitylipoprotein (LDL), high density lipoprotein (HDL), triglycerides, totalcholesterol, ratios of lipid levels such as total cholesterol to HDLratio, insulin sensitivity test, fasting glucose test, Hemoglobin A1Ctest, adipokines such as leptin and adiponectin, neuropeptides such asghrelin, pro-inflammatory cytokines such as interleukin 6 or tumornecrosis factor alpha, anti-inflammatory cytokines such as interleukin10, markers of antioxidant status such as oxidized low-densitylipoprotein, uric acid, paraoxonase 1. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples of physiological state data that may be usedconsistently with descriptions of systems and methods as provided inthis disclosure.

With continued reference to FIG. 1 , physiological data may be obtainedfrom a physically extracted sample. A “physical sample” as used in thisexample, may include any sample obtained from a human body of a user. Aphysical sample may be obtained from a bodily fluid and/or tissueanalysis such as a blood sample, tissue, sample, buccal swab, mucoussample, stool sample, hair sample, fingernail sample and the like. Aphysical sample may be obtained from a device in contact with a humanbody of a user such as a microchip embedded in a user's skin, a sensorin contact with a user's skin, a sensor located on a user's tooth, andthe like. Physiological data may be obtained from a physically extractedsample. A physical sample may include a signal from a sensor configuredto detect physiological data of a user and record physiological data asa function of the signal. A sensor may include any medical sensor and/ormedical device configured to capture sensor data concerning a patient,including any scanning, radiological and/or imaging device such aswithout limitation x-ray equipment, computer assisted tomography (CAT)scan equipment, positron emission tomography (PET) scan equipment, anyform of magnetic resonance imagery (MM) equipment, ultrasound equipment,optical scanning equipment such as photo-plethysmographic equipment, orthe like. A sensor may include any electromagnetic sensor, includingwithout limitation electroencephalographic sensors,magnetoencephalographic sensors, electrocardiographic sensors,electromyographic sensors, or the like. A sensor may include atemperature sensor. A sensor may include any sensor that may be includedin a mobile device and/or wearable device, including without limitationa motion sensor such as an inertial measurement unit (IMU), one or moreaccelerometers, one or more gyroscopes, one or more magnetometers, orthe like. At least a wearable and/or mobile device sensor may capturestep, gait, and/or other mobility data, as well as data describingactivity levels and/or physical fitness. At least a wearable and/ormobile device sensor may detect heart rate or the like. A sensor maydetect any hematological parameter including blood oxygen level, pulserate, heart rate, pulse rhythm, blood sugar, and/or blood pressure. Asensor may be configured to detect internal and/or external biomarkersand/or readings. A sensor may be a part of system 100 or may be aseparate device in communication with system 100.

With continued reference to FIG. 1 , one or more biological extraction124 may be stored in user database 128. User database 128 may beimplemented, without limitation, as a relational database, a key-valueretrieval datastore such as a NOSQL database, or any other form orstructure for use as a datastore that a person skilled in the art wouldrecognize as suitable upon review of the entirety of this disclosure.

With continued reference to FIG. 1 , system 100 is configured toidentify a longevity element 132 associated with a user as a function ofa longevity inquiry 108 and a biological extraction 124. A longevityelement 132 includes any supplement which includes any product intendedto supplement the diet of a human being and/or animal. A supplementincludes any of the supplements as described above. In an embodiment,one or more longevity element 132 may be contained within a longevityinquiry 108. For instance and without limitation, a longevity inquiry108 may include a particular mentioned and/or selected supplement suchas when a user may select a supplement from a drop down menu ongraphical user interface 116. Computing device 104 may include alanguage processing module 136 that may be configured to extract one ormore words from a longevity inquiry 108 and identify one or morelongevity element 132. Language processing module 136 may include anyhardware and/or software module. Language processing module 136 may beconfigured to extract, from one or more inputs, one or more words. Oneor more words may include, without limitation, strings of one orcharacters, including without limitation any sequence or sequences ofletters, numbers, punctuation, diacritic marks, engineering symbols,geometric dimensioning and tolerancing (GD&T) symbols, chemical symbolsand formulas, spaces, whitespace, and other symbols, including anysymbols usable as textual data as described above. Textual data may beparsed into tokens, which may include a simple word (sequence of lettersseparated by whitespace or more generally a sequence of characters asdescribed previously. The term “token,” as used herein, refers to anysmaller, individual groupings of text from a larger source of text;tokens may be broken up by word, pair of words, sentence, or otherdelimitation. These tokens may in turn be parsed in various ways.Textual data may be parsed into words or sequences of words, which maybe considered words as well. Textual data may be parsed into “n-grams”,where all sequences of n consecutive characters are considered. Any orall possible sequences of tokens or words may be stored as “chains”, forexample for use as a Markov chain or Hidden Markov Model.

With continued reference to FIG. 1 , language processing module 136 mayoperate to produce a language processing model. Language processingmodel may include a program automatically generated by a computingdevice 104 and/or language processing module 136 to produce associationsbetween one or more words extracted from at least a document and detectassociations, including without limitation mathematical associations,between such words, and/or associations of extracted words withcategories of longevity inquiries, and/or categories of longevityelement 132. Associations between language elements, where languageelements include for purposes herein extracted words describing and/orincluding constitutional data and/or ameliorative recommendation datamay include, without limitation, mathematical associations, includingwithout limitation statistical correlations between any language elementand any other language element and/or language elements. Statisticalcorrelations and/or mathematical associations may include probabilisticformulas or relationships indicating, for instance, a likelihood that agiven extracted word indicates a given category of a longevity inquiry108, and/or a given category of longevity element 132. As a furtherexample, statistical correlations and/or mathematical associations mayinclude probabilistic formulas or relationships indicating a positiveand/or negative association between at least an extracted word and/or agiven element of constitutional data and/or ameliorative recommendationdata; positive or negative indication may include an indication that agiven document is or is not indicating a longevity element 132. Forinstance, and without limitation, a negative indication may bedetermined from a phrase such as “Vitamin D supplementation was notfound to decrease fracture risk,” whereas a positive indication may bedetermined from a phrase such as “fish oil was found to positivelyimpact cardiovascular health,” as an illustrative example; whether aphrase, sentence, word, or other textual element in a document or corpusof documents constitutes a positive or negative indicator may bedetermined, in an embodiment, by mathematical associations betweendetected words, comparisons to phrases and/or words indicating positiveand/or negative indicators that are stored in memory on a computingdevice 104, or the like.

Still referring to FIG. 1 , language processing module 136 and/or acomputing device 104 may generate a language processing model by anysuitable method, including without limitation a natural languageprocessing classification algorithm; language processing model mayinclude a natural language process classification model that enumeratesand/or derives statistical relationships between input term and outputterms. Algorithm to generate language processing model may include astochastic gradient descent algorithm, which may include a method thatiteratively optimizes an objective function, such as an objectivefunction representing a statistical estimation of relationships betweenterms, including relationships between input terms and output terms, inthe form of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs as used hereinare statistical models with inference algorithms that that may beapplied to the models. In such models, a hidden state to be estimatedmay include an association between an extracted word category of alongevity inquiry 108 and a given relationship of such categories tolongevity element 132. There may be a finite number of categories oflongevity inquires a given relationship and/or a given category oflongevity element 132 to which an extracted word may pertain; an HMMinference algorithm, such as the forward-backward algorithm or theViterbi algorithm, may be used to estimate the most likely discretestate given a word or sequence of words. Language processing module 136may combine two or more approaches. For instance, and withoutlimitation, machine-learning program may use a combination ofNaïve-Bayes (NB), Stochastic Gradient Descent (SGD), and parametergrid-searching classification techniques; the result may include aclassification algorithm that returns ranked associations.

Continuing to refer to FIG. 1 , generating language processing model mayinclude generating a vector space, which may be a collection of vectors,defined as a set of mathematical objects that can be added togetherunder an operation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and is distributive with respect to fieldaddition. Each vector in an n-dimensional vector space may berepresented by an n-tuple of numerical values. Each unique extractedword and/or language element as described above may be represented by avector of the vector space. In an embodiment, each unique extractedand/or other language element may be represented by a dimension ofvector space; as a non-limiting example, each element of a vector mayinclude a number representing an enumeration of co-occurrences of theword and/or language element represented by the vector with another wordand/or language element. Vectors may be normalized, scaled according torelative frequencies of appearance and/or file sizes. In an embodimentassociating language elements to one another as described above mayinclude computing a degree of vector similarity between a vectorrepresenting each language element and a vector representing anotherlanguage element; vector similarity may be measured according to anynorm for proximity and/or similarity of two vectors, including withoutlimitation cosine similarity, which measures the similarity of twovectors by evaluating the cosine of the angle between the vectors, whichcan be computed using a dot product of the two vectors divided by thelengths of the two vectors. Degree of similarity may include any othergeometric measure of distance between vectors.

Still referring to FIG. 1 , language processing module 136 may use acorpus of documents to generate associations between language elementsin a language processing module 136, and a computing device 104 may thenuse such associations to analyze words extracted from one or moredocuments and determine that the one or more documents indicatesignificance of a category of a longevity inquiry 108 and a givenrelationship of such categories to longevity element 132. In anembodiment, a computing device 104 may perform this analysis using aselected set of significant documents, such as documents identified byone or more experts as representing good science, good clinicalanalysis, or the like; experts may identify or enter such documents viaa graphical user interface 116 as described below, or may communicateidentities of significant documents according to any other suitablemethod of electronic communication, or by providing such identity toother persons who may enter such identifications into a computing device104. Documents may be entered into a computing device 104 by beinguploaded by an expert or other persons using, without limitation, filetransfer protocol (FTP) or other suitable methods for transmissionand/or upload of documents; alternatively or additionally, where adocument is identified by a citation, a uniform resource identifier(URI), uniform resource locator (URL) or other datum permittingunambiguous identification of the document, a computing device 104 mayautomatically obtain the document using such an identifier, for instanceby submitting a request to a database or compendium of documents such asJSTOR as provided by Ithaka Harbors, Inc. of New York.

With continued reference to FIG. 1 , computing device 104 may identify alongevity element 132 associated with a user by receiving dietarytraining data 140 wherein dietary training data 140 includes a pluralityof biological extraction 124 and a plurality of correlated longevityelement 132. Training data, as used in this disclosure, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data may include a pluralityof data entries, each entry representing a set of data elements thatwere recorded, received, and/or generated together; data elements may becorrelated by shared existence in a given data entry, by proximity in agiven data entry, or the like. Multiple data entries in training datamay evince one or more trends in correlations between categories of dataelements; for instance, and without limitation, a higher value of afirst data element belonging to a first category of data element maytend to correlate to a higher value of a second data element belongingto a second category of data element, indicating a possible proportionalor other mathematical relationship linking values belonging to the twocategories. Multiple categories of data elements may be related intraining data according to various correlations; correlations mayindicate causative and/or predictive links between categories of dataelements, which may be modeled as relationships such as mathematicalrelationships by machine-learning processes as described in furtherdetail below. Training data may be formatted and/or organized bycategories of data elements, for instance by associating data elementswith one or more descriptors corresponding to categories of dataelements. As a non-limiting example, training data may include dataentered in standardized forms by persons or processes, such that entryof a given data element in a given field in a form may be mapped to oneor more descriptors of categories. Elements in training data may belinked to descriptors of categories by tags, tokens, or other dataelements; for instance, and without limitation, training data may beprovided in fixed-length formats, formats linking positions of data tocategories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),enabling processes or devices to detect categories of data.

Alternatively or additionally, and still referring to FIG. 1 , trainingdata may include one or more elements that are not categorized; that is,training data may not be formatted or contain descriptors for someelements of data. Machine-learning algorithms and/or other processes maysort training data according to one or more categorizations using, forinstance, natural language processing algorithms, tokenization,detection of correlated values in raw data and the like; categories maybe generated using correlation and/or other processing algorithms. As anon-limiting example, in a corpus of text, phrases making up a number“n” of compound words, such as nouns modified by other nouns, may beidentified according to a statistically significant prevalence ofn-grams containing such words in a particular order; such an n-gram maybe categorized as an element of language such as a “word” to be trackedsimilarly to single words, generating a new category as a result ofstatistical analysis. Similarly, in a data entry including some textualdata, a person's name may be identified by reference to a list,dictionary, or other compendium of terms, permitting ad-hoccategorization by machine-learning algorithms, and/or automatedassociation of data in the data entry with descriptors or into a givenformat. The ability to categorize data entries automatedly may enablethe same training data to be ADME applicable for two or more distinctmachine-learning algorithms as described in further detail below.Training data used by computing device 104 may correlate any input dataas described in this disclosure to any output data as described in thisdisclosure.

With continued reference to FIG. 1 , “dietary training data 140” as usedin this disclosure, includes a plurality of data entries each data entrycontaining biological extraction 124 and correlated longevity element132. Dietary training data 140 and/or elements thereof may be entered byone or more users including for example by one or more experts fromremote device 112 and/or from graphical user interface 116. Experts mayinclude one or more physicians, medical experts, nurses, experts in aparticular topic and the like who may hold one or more credentials thatmay certify them as an expert. Credentials may include one or morelicenses such as a medical license or a license to practice in aparticular field of medicine such as a license to prescribe controlledsubstances. Credentials may include one or more board certificationssuch as aa certified personal trainer, a certified group exerciseinstructor, a certified board expert in gastroenterology, a certifiedmedical exercise specialist, a certification from an organizationrelating to functional medicine such as the American Academy ofAnti-Aging Medicine and the like. Credentials may include a particularfield of experience and practice such as a functional medicinesphysician, a rheumatologist, psychiatrist, and the like. Credentials mayinclude publications in top leading medical journals, newspapers, andarticles. Credentials may include participation in one or more clinicaltrials.

With continued reference to FIG. 1 , computing device 104 is configuredto generate using a first machine learning algorithm a dietary model 144correlating biological extraction 124 with longevity element 132.Dietary model 144 may be generated using one or more machine learningprocesses. A machine learning process, also referred to as amachine-learning algorithm, is a process that automatedly uses trainingdata and/or a training set as described above to generate an algorithmthat will be performed by a computing device 104 and/or module toproduce outputs given data provided as inputs; this is in contrast to anon-machine learning software program where the commands to be executedare determined in advance by a user and written in a programminglanguage.

Continuing to refer to FIG. 1 , machine-learning algorithms may beimplemented using techniques for development of linear regressionmodels. Linear regression models may include ordinary least squaresregression, which aims to minimize the square of the difference betweenpredicted outcomes and actual outcomes according to an appropriate normfor measuring such a difference (e.g. a vector-space distance norm);coefficients of the resulting linear equation may be modified to improveminimization. Linear regression models may include ridge regressionmethods, where the function to be minimized includes the least-squaresfunction plus term multiplying the square of each coefficient by ascalar amount to penalize large coefficients. Linear regression modelsmay include least absolute shrinkage and selection operator (LASSO)models, in which ridge regression is combined with multiplying theleast-squares term by a factor of 1 divided by double the number ofsamples. Linear regression models may include a multi-task lasso modelwherein the norm applied in the least-squares term of the lasso model isthe Frobenius norm amounting to the square root of the sum of squares ofall terms. Linear regression models may include the elastic net model, amulti-task elastic net model, a least angle regression model, a LARSlasso model, an orthogonal matching pursuit model, a Bayesian regressionmodel, a logistic regression model, a stochastic gradient descent model,a perceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure,

Still referring to FIG. 1 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

With continued reference to FIG. 1 , models may be generated usingalternative or additional artificial intelligence methods, includingwithout limitation by creating an artificial neural network, such as aconvolutional neural network comprising an input layer of nodes, one ormore intermediate layers, and an output layer of nodes. Connectionsbetween nodes may be created via the process of “training” the network,in which elements from a training dataset are applied to the inputnodes, a suitable training algorithm (such as Levenberg-Marquardt,conjugate gradient, simulated annealing, or other algorithms) is thenused to adjust the connections and weights between nodes in adjacentlayers of the neural network to produce the desired values at the outputnodes. This process is sometimes referred to as deep learning. Thisnetwork may be trained using training data.

Still referring to FIG. 1 , machine-learning algorithms may includesupervised machine-learning algorithms. Supervised machine learningalgorithms, as defined herein, include algorithms that receive atraining set relating a number of inputs to a number of outputs, andseek to find one or more mathematical relations relating inputs tooutputs, where each of the one or more mathematical relations is optimalaccording to some criterion specified to the algorithm using somescoring function. For instance, a supervised machine-learning processmay include a scoring function representing a desired form ofrelationship to be detected between inputs and outputs; scoring functionmay, for instance, seek to maximize the probability that a given inputand/or combination of elements inputs is associated with a given outputto minimize the probability that a given input is not associated with agiven output. Scoring function may be expressed as a risk functionrepresenting an “expected loss” of an algorithm relating inputs tooutputs, where loss is computed as an error function representing adegree to which a prediction generated by the relation is incorrect whencompared to a given input-output pair provided in training data. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various possible variations of supervised machine learningalgorithms that may be used to determine relation between inputs andoutputs.

With continued reference to FIG. 1 , supervised machine-learningprocesses may include classification algorithm defined as processeswhereby a computing device 104 derives, from training data, a model forsorting inputs into categories or bins of data. Classification may beperformed using, without limitation, linear classifiers such as withoutlimitation logistic regression and/or naive Bayes classifiers, nearestneighbor classifiers including without limitation k-nearest neighborsclassifiers, support vector machines, decision trees, boosted trees,random forest classifiers, and/or neural network-based classifiers.

Still referring to FIG. 1 , machine learning processes may includeunsupervised processes. An unsupervised machine-learning process, asused herein, is a process that derives inferences in datasets withoutregard to labels; as a result, an unsupervised machine-learning processmay be free to discover any structure, relationship, and/or correlationprovided in the data. Unsupervised processes may not require a responsevariable; unsupervised processes may be used to find interestingpatterns and/or inferences between variables, to determine a degree ofcorrelation between two or more variables, or the like. Unsupervisedmachine-learning algorithms may include, without limitation, clusteringalgorithms and/or cluster analysis processes, such as without limitationhierarchical clustering, centroid clustering, distribution clustering,clustering using density models, subspace models, group models,graph-based models, signed graph models, neural models, or the like.Unsupervised learning may be performed by neural networks and/or deeplearning protocols as described above.

Continuing to refer to FIG. 1 , machine-learning processes as describedin this disclosure may be used to generate machine-learning models. Amachine-learning model, as used herein, is a mathematical representationof a relationship between inputs and outputs, as generated using anymachine-learning process including without limitation any process asdescribed above, and stored in memory; an input is submitted to amachine-learning model once created, which generates an output based onthe relationship that was derived. For instance, and without limitation,a linear regression model, generated using a linear regressionalgorithm, may compute a linear combination of input data usingcoefficients derived during machine-learning processes to calculate anoutput datum. As a further non-limiting example, a machine-learningmodel may be generated by creating an artificial neural network, such asa convolutional neural network comprising an input layer of nodes, oneor more intermediate layers, and an output layer of nodes. Connectionsbetween nodes may be created via the process of “training” the network,in which elements from a training dataset are applied to the inputnodes, a suitable training algorithm (such as Levenberg-Marquardt,conjugate gradient, simulated annealing, or other algorithms) is thenused to adjust the connections and weights between nodes in adjacentlayers of the neural network to produce the desired values at the outputnodes. This process is sometimes referred to as deep learning.

With continued reference to FIG. 1 , computing device 104 generatesusing a first machine-learning algorithm a dietary model 144 correlatingbiological extraction 124 with longevity element 132. Firstmachine-learning algorithm may include any of the machine-learningalgorithms as described above. Computing device 104 receives a userbiological extraction 124 which may include any of the biologicalextraction 124 as described above. Computing device 104 outputs alongevity element 132 using the first machine-learning algorithm. In anembodiment, computing device 104 may generate using a supervisedmachine-learning algorithm dietary model 144 correlating biologicalextraction 124 with longevity element 132 and utilizing dietary trainingdata 140. Computing device 104 may receive a user biological extraction124 such as for example, a blood sample analyzed for one or moreintracellular and extracellular nutrient levels. Computing device 104may utilize the biological extraction 124 containing the blood sample incombination with a supervised machine-learning algorithm and dietarytraining data 140 to generate dietary model 144 and output a longevityelement 132. Longevity element 132 may include one or more supplementsthat may be selected for the user.

With continued reference to FIG. 1 , computing device 104 is configuredto select an ADME model 148 as a function of a biological extraction124. An “ADME model,” as used in this disclosure, includes any machinelearning model including any mathematical representation of arelationship between inputs that include a longevity element 132associated with a user and outputs that include an ADME factor 152. An“ADME factor 152” as used in this disclosure, includes one or moreelements of data describing absorption, distribution, metabolism, and/orexcretion of one or more longevity element 132. ADME factor 152 maydescribe the absorption, distribution, metabolism and/or excretion ofone particular longevity element 132 such as a specific brand of VitaminC. ADME factor 152 may describe the absorption, distribution, metabolismand/or excretion of a generic category of longevity element 132 such asVitamin C which could include multiple different brands of Vitamin C.ADME factor 152 may describe the absorption, distribution, metabolism,and/or excretion of a category of longevity element 132 such as jointsupport longevity element 132 or vision support longevity element 132.Absorption may include the ability of a longevity element 132 to reach atissue such as via mucous surfaces such as intestinal absorption in thedigestive tract. Absorption may be altered by factors such as poorsolubility of a longevity element 132. For example, vitamin B12 such asmethylcobalamin contains very poor oral absorption and solubility, andas such is preferably administered as a sublingual tablet, as a nasalspray or as an injection. Absorption may also be altered by otherfactors such as gastric emptying time which can be affected by medicalconditions such as diabetes that can cause gastroparesis and delayedgastrointestinal emptying time. Absorption may be altered by chemicalinstability of a longevity element 132 in the stomach, and the inabilityof a longevity element 132 to permeate the intestinal wall therebyreducing the extent to which a longevity element 132 is absorbed.Absorption may also affect bioavailability of a longevity element 132 aslongevity element 132 that are poorly absorbed may have very littlebioavailability and as such may need to be administered in analternative dosage form. Distribution may include the ability of alongevity element 132 to be carried to its effector site, such asthrough the bloodstream. After passage through the bloodstream, alongevity element 132 may be distributed to one or more muscles andorgans. The ability of a longevity element 132 to be distributed to oneor more locations in the body may be affected by factors such asregional blood flow rates, molecular size, polarity and binding to serumproteins, and forming a complex. For example, a longevity element 132such as levocarnitine is unable to be distributed across the blood brainbarrier and as such acts systemically in the body outside of the bloodbrain barrier, while acetyl-1-carnitine is able to be distributed acrossthe blood brain barrier and is effectively utilized for neurologicalconditions including memory issues and tremors seen in individuals withParkinson's disease. Metabolism includes the ability of a longevityelement 132 to be broken down as it enters the body. Metabolism may becarried out by the liver through redox enzymes or cytochrome P450enzymes. As a longevity element 132 is metabolized, it may be convertedto one or more new compounds known as metabolites. Excretion includesthe ability of a longevity element 132 and its metabolites to be removedfrom the body via excretion such as through the kidneys and eventuallyinto urine and/or in the feces. Excretion can occur at the kidneys wherea longevity element 132 is excreted into urine. Excretion can occur inbiliary tract where excretion begins in the liver and passes through tothe gut until the longevity element 132 is excreted in urine or fecalelimination. Excretion can occur through the lungs such as by exhaling alongevity element 132 or its metabolites.

With continued reference to FIG. 1 , computing device 104 may select anADME model 148 from model database 156. Model database 156 may includeany data structure suitable for use as user database 128 as describedabove. Computing device 104 may select an ADME model 148 by firstidentifying a genetic marker 160 contained within a biologicalextraction 124. A “genetic marker 160” as used in this disclosure, is agene, deoxyribonucleic acid (DNA) sequence, and/or ribonucleic acid(RNA) sequence that may be utilized to identify individuals and/orspecies. A genetic marker 160 may include a variation such as a DNAsequence that contains a single base-pair change such as a singlenucleotide polymorphism (SNP). A genetic marker 160 may include avariation such as a minisatellite where a certain DNA motif which mayrange in length from between 10 to 60 based pairs may be repeated 5-50times. A genetic marker 160 may include a biochemical marker which maybe utilized to detect variation at a gene product level such as a changein proteins or amino acids. A genetic marker 160 may include a molecularmarker which may detect a variation of a nucleotide change such as adeletion, duplication, inversion, and/or insertion. A genetic marker 160may include a genotype which may include a set of one or more genes. Agenetic marker 160 may include a phenotype which may include one or moreobservable characteristics of an individual resulting from theinteraction of its genotype with the environment. A genetic marker 160may include one or more markers of cytochrome p450 enzymes such as oneor more phenotypes that may be involved in the metabolism of one or morelongevity element 132. For example, a genetic marker 160 such as aCYP2C9*2 phenotype may be associated with reduced enzymatic activity andthus reduced metabolism of a longevity element 132 metabolized throughthe CYP2C9 pathway. In yet another non-limiting example, a geneticmarker 160 such as a CYP2D6*2A phenotype may be associated with rapidenzymatic activity and thus increased metabolism of a longevity element132 metabolized through the CYP2D6 pathway. A genetic marker 160 mayinclude any of the genetic body measurements as described above. Forinstance and without limitation, a genetic marker 160 may include an A/Ggenotype of the CYP17A1 gene responsible for producing an enzymeresponsible for converting progesterone into androgens. An A/G genotypeindicates a fast conversion thus indicating estrogen dominance while anA/A genotype indicates normal conditions.

With continued reference to FIG. 1 , computing device 104 generatesusing genetic training data 164 and using a classification algorithm, agenetic classifier 168, wherein the genetic classifier 168 inputs agenetic marker 160 and outputs an ADME model 148. “Genetic training data164” as used in this disclosure, includes a plurality of data entries,each data entry containing genetic marker 160 and correlated ADME model148. Computing device 104 may generate genetic classifier 168 using aclassification algorithm 172, defined as a process whereby a computingdevice 104 derives, from training data, a model known as a “classifier”for sorting inputs into categories or bins of data. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naïve Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers.

With continued reference to FIG. 1 , computing device 104 may beconfigured to generate genetic classifier 168 using a Naïve Bayesclassification algorithm 172. Naïve Bayes classification algorithm 172generates classifiers by assigning class labels to problem instances,represented as vectors of element values. Class labels are drawn from afinite set. Naïve Bayes classification algorithm 172 may includegenerating a family of algorithms that assume that the value of aparticular element is independent of the value of any other element,given a class variable. Naïve Bayes classification algorithm 172 may bebased on Bayes Theorem expressed as P(A/B)=P(B/A) P(A)±P(B), whereP(A/B) is the probability of hypothesis A given data B also known asposterior probability; P(B/A) is the probability of data B given thatthe hypothesis A was true; P(A) is the probability of hypothesis A beingtrue regardless of data also known as prior probability of A; and P(B)is the probability of the data regardless of the hypothesis. A naïveBayes algorithm may be generated by first transforming training datainto a frequency table. Computing device 104 may then calculate alikelihood table by calculating probabilities of different data entriesand classification labels. Computing device 104 may utilize a naïveBayes equation to calculate a posterior probability for each class. Aclass containing the highest posterior probability is the outcome ofprediction. Naïve Bayes classification algorithm 172 may include agaussian model that follows a normal distribution. Naïve Bayesclassification algorithm 172 may include a multinomial model that isused for discrete counts. Naïve Bayes classification algorithm 172 mayinclude a Bernoulli model that may be utilized when vectors are binary.

With continued reference to FIG. 1 , computing device 104 may beconfigured to generate genetic classifier 168 using a K-nearestneighbors (KNN) algorithm. A “K-nearest neighbors algorithm” as used inthis disclosure, includes a classification method that utilizes featuresimilarity to analyze how closely out-of-sample-features resembletraining data to classify input data to one or more clusters and/orcategories of features as represented in training data; this may beperformed by representing both training data and input data in vectorforms, and using one or more measures of vector similarity to identifyclassifications within training data, and to determine a classificationof input data. K-nearest neighbors algorithm may include specifying aK-value, or a number directing the classifier to select the k mostsimilar entries training data to a given sample, determining the mostcommon classifier of the entries in the database, and classifying theknown sample; this may be performed recursively and/or iteratively togenerate a classifier that may be used to classify input data as furthersamples. For instance, an initial set of samples may be performed tocover an initial heuristic and/or “first guess” at an output and/orrelationship, which may be seeded, without limitation, using expertinput received according to any process as described herein. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data. Heuristic mayinclude selecting some number of highest-ranking associations and/ortraining data elements.

With continued reference to FIG. 1 , generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute l as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n)α_(i) ²)}, whereα_(i) is attribute number i of the vector. Scaling and/or normalizationmay function to make vector comparison independent of absolutequantities of attributes, while preserving any dependency on similarityof attributes; this may, for instance, be advantageous where casesrepresented in training data are represented by different quantities ofsamples, which may result in proportionally equivalent vectors withdivergent values. As a non-limiting example, K-nearest neighborsalgorithm may be configured to classify an input vector including aplurality of genetic marker 160, to clusters representing ADME model148.

With continued reference to FIG. 1 , genetic classifier 168 may becustomized to a particular user. For instance and without limitation,genetic classifier 168 may receive genetic training data 164 asdescribed which may be modified and/or updated matching identifiedgenetic marker 160 and/or previously selected ADME model 148. Forinstance and without limitation, if a user has a particular geneticmarker 160 such as a single nucleotide polymorphism (SNP) of abeta-globin gene resulting in glutamic acid being substituted by valineat position 6, thereby resulting in sickle cell anemia. In such aninstance, a particular ADME model 148 may be selected as a result of theSNP. In such an instance, genetic classifier 168 may learn theparticular ADME model 148 selected as a result of the SNP and may selectthe same ADME model 148 at a later point in time when presented with thesame SNP for the same user. In yet another non-limiting example, genetictraining data 164 may be updated resulting in a correlation between theabove mentioned SNP of the beta-globin gene to select a different ADMEmodel 148 that more closely hews to the SNP. In such an instance,genetic classifier 168 may select this ADME model 148 that more closelyhews to the SNP as a result of learning this updated associationreceived through updated genetic training data 164.

With continued reference to FIG. 1 , computing device 104 selects anADME model 148 as a function of generating a genetic classifier 168.Genetic classifier 168 may output an ADME model 148 utilizing a geneticmarker 160 contained within a biological extraction 124. Computingdevice 104 may select an ADME model 148 by retrieving a biologicalextraction 124 from user database 128 wherein the biological extraction124 includes a genetic marker 160 containing an ADME marker. An “ADMEmarker” as used in this disclosure, includes any indication as to theADME profile of the biological extraction 124. Computing device 104 maylabel a genetic marker 160 with an ADME marker retrieved from a databasesuch as from model database 156. For instance and without limitation, agenetic marker 160 such as an A/G genotype of the MCM6 gene thatcontrols production of the lactase enzyme may contain an ADME markerthat indicates a SNP of the MCM6 gene. In yet another non-limitingexample, a genetic marker 160 that contains a substitution of a basesuch as purine for cytosine may contain an ADME marker that indicates asubstitution mutation of the particular gene that is affected. Computingdevice 104 may utilize a biological extraction 124 containing an ADMEmarker to locate an ADME model 148 containing the ADME marker. Forinstance and without limitation, computing device 104 may locate an ADMEmodel 148 within model database 156 containing an ADME marker thatmatches the ADME marker contained within a biological extraction 124.

With continued reference to FIG. 1 , computing device 104 generates amachine-learning algorithm 176 utilizing an ADME model 148 that inputs alongevity element 132 associated with the user as an input and outputsan ADME factor 152. Machine-learning algorithm 176 includes any of themachine-learning algorithm 176 as described above. An ADME factor 152includes any of the ADME factor 152 as described above, including one ormore elements of data describing absorption, distribution, metabolism,and/or excretion of one or more longevity element 132. For instance andwithout limitation, an ADME factor 152 may include a description that aparticular brand of immediate release turmeric will be poorly absorbedby a user due to an altered gastrointestinal microbiome, but aparticular brand of slow release turmeric will have greaterbioavailability and demonstrate a better rate of absorption. In yetanother non-limiting example, an ADME factor 152 may indicate that auser who has impaired renal function may be better suited consuming alongevity element 132 that is metabolized hepatically as compared to alongevity element 132 that is metabolized renally. In yet anothernon-limiting example, a longevity element 132 may indicate that aparticular genetic mutation such as a SNP that the user has may altermetabolism of a longevity element 132 when administered as a liquid orcapsule, but that metabolism will not be affected if the longevityelement 132 is applied as a transdermal cream.

With continued reference to FIG. 1 , computing device 104 identifies atolerant longevity element 180 as a function of an ADME factor 152. A“tolerant longevity element 180” as used in this disclosure, includesany longevity element 132 that is compatible with a selected user.Compatibility includes any longevity element 132 that contributes toachieving and/or maintaining vibrant health and longevity, and whichdoes not deter and result in an incompatibility with a user. Forinstance and without limitation, a longevity element 132 such as aparticular brand of fish oil that is poorly absorbed and that does notpenetrate into the gastrointestinal tract of a user may be incompatibleand as such would not be deemed to be a tolerant longevity element 180.In such an instance, the particular brand of fish oil may appear to bewell absorbed and well metabolized by a second user and as such may beconsidered a tolerant longevity element 180 for the second user.Selecting a tolerant longevity element 180 may include selecting atolerant longevity element 180 that is compatible with an ADME factor152. For instance and without limitation, an ADME factor 152 mayindicate that a user has impaired functioning mucous cells and as such adosage form such as a capsule form of a longevity element 132 or atablet form of a longevity element 132 may not be absorbed. However,ADME factor 152 may indicate that a liquid dosage form of a longevityelement 132 may be absorbed, and as such a longevity element 132 may beselected that contains a liquid dosage form.

With continued reference to FIG. 1 , identifying a tolerant longevityelement 180 may include identifying a second longevity element 132administered in conjunction with an identified tolerant longevityelement 180. For instance and without limitation, a tolerant longevityelement 180 such as zinc may cause computing device 104 to identify asecond longevity element 132 such as copper administered in conjunctionwith zinc. In yet another non-limiting example, a tolerant longevityelement 180 such as chaste tree berry may cause computing device 104 toidentify a second longevity element 132 such as diindolylmethaneadministered in conjunction with chaste tree berry. Computing device 104may generate a second ADME factor 152 for a second longevity element 132and identify a second tolerant longevity element 180 as a function ofthe second ADME factor 152. For example, computing device 104 mayidentify a second longevity element 132 such as Vitamin C administeredin conjunction with an identified tolerant longevity element 180 such asiron. Computing device 104 generates a second ADME factor 152 for theVitamin C, and identifies a second tolerant longevity element 180utilizing the second ADME factor 152 for the Vitamin C.

With continued reference to FIG. 1 , identifying a tolerant longevityelement 180 includes identifying a second longevity element 132contraindicated with an identified tolerant longevity element 180 andeliminating the second longevity element 132 as a tolerant longevityelement 180. For instance and without limitation, an identified secondlongevity element 132 such as valerian root may be identified as beingcontraindicated with an identified tolerant longevity element 180 suchas St. John's wort and as such computing device 104 may eliminatevalerian root as a tolerant longevity element 180. In yet anothernon-limiting example, an identified second longevity element 132 such ascalcium may be identified as being contraindicated with an identifiedtolerant longevity element 180 such as magnesium and as such computingdevice 104 may eliminate calcium as a tolerant longevity element 180.

With continued reference to FIG. 1 , identifying a tolerant longevityelement 180 includes identifying a first tolerant longevity element 180containing a first active ingredient and identifying a second tolerantlongevity element 180 containing a second active ingredient. An “activeingredient” as used in this disclosure, includes one or more ingredientspresent in a longevity element 132 that are biologically active. Anactive ingredient may also be referred to as an active substance. Forinstance and without limitation, computing device 104 may identify afirst tolerant longevity element 180 such as a multivitamin containing afirst active ingredient such as Vitamin B12 and a second tolerantlongevity element 180 such as a B-complex containing a second activeingredient that includes Vitamin B12. Computing device 104 may determinethat the first active ingredient of Vitamin B12 relates to the secondactive ingredient of Vitamin B12 and as such may eliminate the secondtolerant longevity element 180 that includes Vitamin B12. A first activeingredient relating to a second active ingredient includes anyduplication of active ingredients, contraindication of activeingredients, and/or excess supplementation of ingredients. A duplicationof active ingredients may occur when a first active ingredient is thesame as a second active ingredient. For instance and without limitation,a first active ingredient may include Vitamin D3 and a second activeingredient may include Vitamin D3. A contraindication of activeingredients may occur when a first active ingredient should not beconsumed in combination with a second active ingredient. For instanceand without limitation, a first active ingredient may include calciumand a second active ingredient may include iron. An excesssupplementation of ingredients may occur when consumption of a firstactive ingredient in combination with a second active ingredient maycause an excess dose or quantity of ingredients. For instance andwithout limitation, a first active ingredient may include a multivitamincontaining various b-vitamins and a second active ingredient may includea b-complex which may contain various other b-vitamins not containedwithin the multivitamin but when given in combination may cause anoverdose of total b-vitamins.

Referring now to FIG. 2 , an exemplary embodiment 200 of user database128 is illustrated. User database 128 may be implemented as any datastructure as described above in reference to FIG. 1 . One or more tablescontained within user database 128 may include microbiome sample table204; microbiome sample table 204 may include one or more biologicalextraction 124 relating to the microbiome. For instance and withoutlimitation, microbiome sample table 204 may include a physicallyextracted sample such as a stool sample analyzed for the presence ofpathogenic species such as parasites and anaerobes. One or more tablescontained within user database 128 may include fluid sample table 208;fluid sample table 208 may include one or more biological extraction 124containing fluid samples. For instance and without limitation, fluidsample table 208 may include a urine sample analyzed for the presence orabsence of glucose. One or more tables contained within user database128 may include intracellular nutrient data table 212; intracellularnutrient data table 212 may include one or more biological extraction124 containing intracellular nutrient levels. For instance and withoutlimitation, intracellular nutrient data table 212 may include a bloodsample analyzed for intracellular levels of Vitamin B12. One or moretables contained within user database 128 may include microchip sampletable 216; microchip sample table 216 may include one or more biologicalextraction 124 obtained from a microchip. For instance and withoutlimitation, microchip sample table 216 may include a blood sugar levelobtained from a microchip embedded under a user's skin. One or moretables contained within user database 128 may include demographic table220; demographic table 220 may include one or more demographic inputspertaining to a user. For instance and without limitation, demographictable 220 may include information pertaining to a user's full name,address, date of birth, sex, marital status, occupation, and the like.One or more tables contained within user database 128 may includegenetic marker table 224; genetic marker table 224 may include one ormore biological extraction 124 containing one or more genetic marker160. For instance and without limitation, genetic marker table 224 mayinclude a blood sample result analyzed for the genotype of the MCM6 geneof a user.

Referring now to FIG. 3 , an exemplary embodiment 300 of dietary model144 is illustrated. Computing device 104 identifies a longevity element132 associated with a user by receiving dietary training data 140.Dietary training data 140 includes any of the dietary training data 140as described above in reference to FIG. 1 . Dietary training data 140includes a plurality of biological extraction 124 and a plurality ofcorrelated longevity element 132. For instance and without limitation,dietary training data 140 may include a biological extraction 124 suchas a saliva sample containing one or more salivary hormone levels suchas elevated estradiol and estrone levels correlated to one or morelongevity element 132 such as calcium d-glucarate, milk thistle,dandelion root, and chaste tree berry. Computing device 104 may includemachine-learning module 304. Machine-learning module may be implementedas any hardware and/or software module. Machine-learning module 304 maybe configured to calculate one or more machine-learning algorithm 176.Machine-learning algorithm 176 include any of the machine-learningalgorithm 176 as described above. For instance and without limitation,machine-learning module 304 may generate a supervised machine-learningalgorithm 176 utilizing a user biological extraction 124 retrieved fromuser database 128 as an input and outputting a longevity element 132.Machine-learning module 304 may generate an unsupervisedmachine-learning algorithm 176 utilizing a user biological extraction124 retrieved from user database 128 as an input and outputting alongevity element 132. Machine-learning module 304 may generate alazy-learning algorithm utilizing a user biological extraction 124retrieved from user database 128 as an input and outputting a longevityelement 132. Machine-learning module 304 may generate one or moremachine-learning algorithm 176 including a combination of one or morealgorithms as described above. Machine-learning module 304 generatesusing a first machine-learning model dietary model 144 correlatingbiological extraction 124 with longevity element 132. Dietary model 144may include any machine learning process and may include linear orpolynomial regression algorithms. Dietary model 144 may include one ormore equations. Dietary model 144 may include a set of instructionsutilized to generate outputs based on inputs derived using amachine-learning algorithm 176 and the like. Dietary model 144 outputs alongevity element 132. Longevity element 132 includes any of thelongevity element 132 as described above in reference to FIG. 1 .

Referring now to FIG. 4 , an exemplary embodiment 400 of model database156 is illustrated. Model database 156 may be implemented as any datastructure suitable for use as user database 128 as described above inmore detail in reference to FIG. 1 . One or more tables contained withinmodel database 156 may include ADME model 148 data table 404; ADME model148 data table 404 may include one or more data entries containing ADMEmodel 148. One or more tables contained within model database 156 mayinclude dietary model 144 data table 408; dietary model 144 data tablemay include one or more data entries containing one or more dietarymodel 144. One or more tables contained within model database 156 mayinclude ADME factor 152 table 412; ADME factor 152 table 412 may includeone or more data entries containing one or more ADME factor 152. One ormore tables contained within model database 156 ADME includeclassification algorithm 172 data table 416; classification algorithm172 data table 416 may include one or more data entries containing oneor more classification algorithm 172. One or more tables containedwithin model database 156 may include machine-learning algorithm 176table 420; machine-learning algorithm 176 table 420 may include one ormore data entries containing one or more machine-learning algorithm 176.One or more tables contained within model database 156 may includelongevity element 132 data table 424; longevity element 132 data table424 may include one or more data entries containing one or morelongevity element 132.

Referring now to FIG. 5 , an exemplary embodiment of genetic classifier168 is illustrated. Genetic classifier 168 may be generated by computingdevice 104 utilizing any of the methodologies as described above.Computing device 104 identifies a genetic marker 160 contained within abiological extraction 124. One or more biological extraction 124 may bestored in user database 128 as described above in more detail inreference to FIGS. 1-2 . Genetic marker 160 includes any of the geneticmarker 160 as described above in reference to FIG. 1 . For instance andwithout limitation, genetic marker 160 may include a salivary sampleanalyzed for a user's genotype for the NOS3 gene responsible forcontrolling production of enzymes involved in regulating vaso-dilationand vaso-constriction. In yet another non-limiting example, geneticmarker 160 may include a blood sample analyzed for a user's genotype forthe SLCO1B1 gene responsible for directing influx efficiency ofpharmaceuticals into cells. Computing device 104 generates using genetictraining data 164 and using a classification algorithm 172 a geneticclassifier 168. Genetic training data 164 includes any of the genetictraining data 164 as described above. Genetic training data 164 includesa plurality of data entries containing genetic marker 160 and correlatedADME model 148. Classification algorithm 172 include any of theclassification algorithm 172 as described above, including for examplelinear classifiers, fisher's linear discriminant, support vectormachines, quadratic classifiers, kernel estimation, k-nearest neighbor,decision trees, random forests, neural networks, learning vectorquantization and the like.

With continued reference to FIG. 5 , computing device 104 selects anADME model 148 as function of generating genetic classifier 168. In anembodiment, ADME model 148 A 504 may include a model that is best suitedfor a user with slow intestinal transit time, impaired distribution,normal metabolism, and normal elimination. In an embodiment, ADME model148 B 508 may include a model that is best suited for a user with normalabsorption, normal distribution, impaired hepatic metabolism, and fecalelimination only. In an embodiment, ADME model 148 C 512 may include amodel that is best suited for a user with normal absorption, impaireddistribution, normal metabolism, and normal elimination. Geneticclassifier 168 may select an ADME model 148 from ADME model 148 X 516 oran indefinite number of ADME model 148. Selected ADME model 148 520 isutilized by computing device 104 to generate a machine-learningalgorithm 176 utilizing the ADME model 148 that inputs a longevityelement 132 associated with a user as an input and outputs an ADMEfactor 152.

Referring now to FIG. 6 , an exemplary embodiment of machine-learningalgorithm 176 is illustrated. Computing device 104 generates amachine-learning algorithm 176 utilizing the selected ADME model 148.Computing device 104 may retrieve the selected ADME model 148 from modeldatabase 156. ADME model 148 may be selected utilizing any of themethodologies as described herein. Computing device 104 generates amachine-learning algorithm 176, including any of the machine-learningalgorithm 176 as described above. For instance and without limitation,computing device 104 may generate a machine-learning algorithm 176 suchas a supervised machine-learning algorithm 176, an unsupervisedmachine-learning algorithm 176, linear regression, logistic regression,decision tree, naïve bayes, k-nearest neighbor-k-means clustering,random forest, and the like. Selected ADME model 148 utilizes alongevity element 132 associated with the user as an input and outputsan ADME factor 152. ADME factor 152 includes any of the ADME factor 152as described above. For instance and without limitation, a longevityelement 132 associated with the user may include a fish oil supplement.ADME factor 152 may be utilized to select one or more longevity element132 containing various doses, dosage forms, flavors, and the like offish oil. ADME factor 152 may be utilized to select between for example,longevity element 132 A 604 which may contain a liquid unflavored formof fish oil. ADME factor 152 may be utilized to select between forexample, longevity element 132 B 608 which may include a liquid flavoredfish oil containing orange sherbet flavor. ADME factor 152 may beutilized to select between for example, longevity element 132 C 612which may include a capsule form of fish oil containing 500 mg in eachcapsule. ADME factor 152 may be utilized to select longevity element 132X or an indefinite number of longevity element 132. Computing device 104selects a tolerant longevity element 180 utilizing ADME factor 152 asdescribed above in reference to FIG. 1 .

Referring now to FIG. 7 , an exemplary embodiment of a method 700 ofoptimizing supplement decisions is illustrated. At step 705 a computingdevice 104 receives a longevity inquiry 108 from a remote device 112.Computing device 104 receives a longevity inquiry 108 utilizing anynetwork methodology as described herein. Longevity inquiry 108 includesany of the longevity inquires as described above in reference to FIG. 1. For instance and without limitation, a longevity inquiry 108 mayinclude a question regarding if a user should consume a particularsupplement such as a ubiquinol supplement recommended by the user'sfriend. In yet another non-limiting example, a longevity inquiry 108 mayinclude a description of a particular symptom that a user experiencessuch as a description of stabbing back pain upon waking and liftinganything over five pounds and an inquiry as to a supplement that a usercan consume to reduce stabbing back pain. In yet another non-limitingexample, a longevity inquiry 108 may include a selection of one or moresupplements and/or supplement categories that may be displayed ongraphical user interface 116 located on computing device. In anembodiment, computing device 104 may display a picture of one or morelongevity element 132 that a user may select to determine if the usershould consume the particularly selected longevity element 132. In anembodiment, a longevity inquiry 108 may be generated by a family member,friend, neighbor, spouse, boyfriend, girlfriend, informed advisor andthe like inquiring as to if the user should consume a particularsupplement, a description of one or more symptoms that the user may beexperiencing and the like. For example, an informed advisor such asuser's yoga teacher may generate a longevity inquiry 108 after a yogaclass where user complains of stiff muscles, pain in user's neck, andlow back spasm. In an embodiment, a longevity inquiry 108 may include aparticular brand of supplement with an inquiry from a user seeking toknow if the particular brand of supplement is compatible with the user'sbody. Computing device 104 receives a longevity inquiry 108 from aremote device 112, which may include any of the remote device 112 asdescribed above in reference to FIG. 1 .

With continued reference to FIG. 7 , receiving a longevity inquiry 108may include receiving a photograph of a longevity element 132. Computingdevice 104 may receive at an image device 120 located on computingdevice 104 a wireless transmission from remote device 112 containing aphotograph of a longevity element 132. Longevity element 132 includesany of the longevity element 132 as described above in reference toFIGS. 1-6 . Image device 120 includes any of the image device 120 asdescribed above in reference to FIGS. 1-6 . In an embodiment, a user maytake a photograph of a particular longevity element 132 such as whenuser is shopping in a health food store with a camera located on user'smobile phone. In such an instance, image device 120 located on computingdevice 104 may receive the photograph of the longevity element 132. Inan embodiment, a photograph of a longevity element 132 may include aphotograph of a unique identifier of a longevity element 132 such as auniversal product code (UPC) containing 12 numeric digits uniquelyidentifying a longevity element 132

With continued reference to FIG. 7 , at step 710 a computing device 104retrieves a biological extraction 124 from a user database 128.Biological extraction 124 includes any of the biological extraction 124as described above in reference to FIG. 1 . For instance and withoutlimitation, a biological extraction 124 may include a stool sampleanalyzed for one or more markers of digestion and absorption such aschymotrypsin, putrefactive short-chain fatty acids, meat and vegetablefibers, and fecal fats. In yet another non-limiting example, abiological extraction 124 may include a hair sample analyzed for levelsof one or more toxic elements such as lead, mercury, arsenic, bismuth,cesium, platinum, tin, and uranium. One or more biological extraction124 may be stored in user database 128 which may include any datastructure as described above in reference to FIGS. 1-2 .

With continued reference to FIG. 7 , at step 715 a computing device 104identifies a longevity element 132 associated with a user as a functionof a longevity inquiry 108 and a biological extraction 124. Longevityelement 132 includes any of the longevity element 132 as described abovein reference to FIGS. 1-6 . In an embodiment, a longevity element 132may be contained within a longevity inquiry 108 such as when a longevityinquiry 108 may name a particular supplement brand or category ofsupplement. For instance and without limitation, a longevity inquiry 108may include a question as to the best multivitamin product that a usershould consume. In yet another non-limiting example, a longevity inquiry108 may include a description of a particular supplement brand such as365 everyday Value Folic Acid as produced by WHOLE FOODS MARKET ofAustin, Texas Computing device 104 may utilize language processingmodule 136 to extract one or more longevity element 132 contained withina longevity inquiry 108. This may be performed utilizing any of themethodologies as described above in reference to FIG. 1 .

With continued reference to FIG. 7 , identifying a longevity element 132associated with a user may include generating a machine-learningalgorithm 176. Computing device 104 may receive dietary training data140. Dietary training data 140 may include any of the dietary trainingdata 140 as described above in reference to FIG. 1 . Dietary trainingdata 140 may include a plurality of data entries including biologicalextraction 124 correlated to longevity element 132. Computing device 104may receive dietary training data 140 from model database 156. Computingdevice 104 generates using a first machine-learning algorithm 176 adietary model 144 correlating biological extraction 124 with longevityelement 132. Dietary model 144 includes any of the dietary model 144 asdescribed above. Dietary model 144 may include one or more equations.Dietary model 144 may include any machine-learning process and mayinclude linear or polynomial regression algorithms. Dietary model 144may include one or more supervised machine-learning algorithm 176.Dietary model 144 may include one or more unsupervised machine-learningalgorithm 176. Dietary model 144 may include one or more lazy learningmodels. Dietary model 144 may include a set of instructions utilized togenerate outputs based on inputs derived using one or moremachine-learning algorithm 176. Computing device 104 receives a userbiological extraction 124 and outputs a longevity element 132 using afirst machine-learning algorithm 176. Longevity element 132 includes anyof the longevity element 132 as described herein.

With continued reference to FIG. 7 , at step 720 a computing device 104selects an ADME model 148 as a function of a biological extraction 124.ADME model 148 includes any of the ADME model 148 as described above inreference to FIGS. 1-6 . Computing device 104 may select an ADME model148 utilizing any of the methodologies as described above. Computingdevice 104 may select an ADME model 148 by generating genetic classifier168. Computing device 104 identifies a genetic marker 160 containedwithin a biological extraction 124. Genetic marker 160 includes any ofthe genetic marker 160 as described above in reference to FIGS. 1-6 .For instance and without limitation, a biological extraction 124 such asa blood sample may contain a genetic marker 160 that includes a user T/Tgenotype of the MTHFR gene. In yet another non-limiting example, abiological extraction 124 such as a salivary sample may contain agenetic marker 160 that includes a user G/A genotype of the SHMT1 gene.Computing device 104 generates using genetic training data 164 includinga plurality of genetic marker 160 and a plurality of correlated ADMEmodel 148 and using a classification algorithm 172, genetic classifier168. This may be performed utilizing any of the methods as describedabove in reference to FIGS. 1-6 . Genetic classifier 168 inputs agenetic marker 160 and outputs an ADME model 148. For instance andwithout limitation, a genetic marker 160 such as a G/G genotype of theMTR gene indicating suboptimal production of enzymes involved inmetabolism and utilization by the body of Vitamin B12 may be utilized bygenetic classifier 168 in combination with generating a classificationalgorithm 172 and genetic training data 164 to select an ADME model 148that contains impaired absorption, adequate distribution, impairedmetabolism, and adequate elimination. Computing device 104 selects anADME model 148 as a function of generating a genetic classifier 168.This may be performed utilizing any of the methodologies as describedabove in reference to FIGS. 1-6 .

With continued reference to FIG. 7 , selecting an ADME model 148 mayinclude retrieving a biological extraction 124 from user database 128wherein the biological extraction 124 includes a genetic marker 160containing an ADME marker and locating an ADME model 148 containing theADME marker. ADME marker includes any indication as to the ADME profileof the biological extraction 124. For instance and without limitation, abiological extraction 124 containing an A/A genotype of the MTR genethat controls metabolism of Vitamin B12 as described above may containan ADME marker that labels the genetic marker 160 as optimal because theA/A genotype is the expected genotype assuming no SNPs or mutations.Similarly, a biological extraction 124 containing a G/G genotype of theMTR gene may contain an ADME marker that labels the genetic marker 160as suboptimal because of the increased risk of elevated homocysteine andassociated health risks such as increased risk for a myocardialinfarction and heart disease seen in users with the G/G genotype but notthe A/A genotype. Computing device 104 locates an ADME model 148containing an ADME marker that matches the ADME marker contained on thebiological extraction 124. Computing device 104 may retrieve one or moreADME model 148 from model database 156. Biological extraction 124containing an ADME marker may be stored in user database 128.

With continued reference to FIG. 7 , at step 725 a computing device 104generates a machine-learning algorithm 176 utilizing an ADME model 148that inputs a longevity element 132 associated with the user as an inputand outputs an ADME factor 152. ADME model 148 includes any of the ADMEmodel 148 as described above. ADME model 148 inputs a longevity element132 associated with a user and outputs an ADME factor 152. ADME factor152 includes one or more elements of data describing absorption,distribution, metabolism, and/or excretion of one or more longevityelement 132. ADME factor 152 includes any of the ADME factor 152 asdescribed above in reference to FIG. 1 .

With continued reference to FIG. 7 , at step 730 a computing device 104identifies a tolerant longevity element 180 as a function of an ADMEfactor 152. Tolerant longevity element 180 includes any of the tolerantlongevity element 180 as described above in reference to FIGS. 1-6 .Identifying a tolerant longevity element 180 includes selecting atolerant longevity element 180 compatible with an ADME factor 152. Forinstance and without limitation, an ADME factor 152 that indicatesimpaired gastrointestinal absorption may be utilized to select atolerant longevity element 180 that does not require gastrointestinalabsorption such as in an injectable dosage form or a suppository dosageform intended for rectal or vaginal administration. In yet anothernon-limiting example, an ADME factor 152 that indicates impaired hepaticelimination may be utilized to select a tolerant longevity element 180that does not require hepatic elimination but rather is almost solelyrenally eliminated into urine. Identifying a tolerant longevity element180 includes identifying by computing device 104 a second longevityelement 132 administered in conjunction with an identified tolerantlongevity element 180. For instance and without limitation, anidentified tolerant longevity element 180 containing calcium carbonatemay cause computing device 104 to identify Vitamin D which can beadministered in conjunction with calcium carbonate. Computing device 104generates a second ADME factor 152 for the Vitamin D, utilizing any ofthe methodologies as described above. Computing device 104 identifies asecond tolerant longevity element 180 as a function of the second ADMEfactor 152. For example, computing device 104 may identify a particularbrand of Vitamin D that user may tolerate and that may be administeredin conjunction with calcium carbonate. In an embodiment, second ADMEfactor 152 may cause computing device 104 to not be able to identify asecond tolerant longevity element 180. In such an instance, user may beinstructed only to consume first tolerant longevity element 180.

With continued reference to FIG. 7 , identifying a tolerant longevityelement 180 includes identifying a second longevity element 132contraindicated with an identified tolerant longevity element 180 andeliminating the second longevity element 132 as a tolerant longevityelement 180. For instance and without limitation, computing device 104may identify a tolerant longevity element 180 such as melatonin for auser with insomnia. Computing device 104 may identify a second longevityelement 132 such as 5-hydroxytryptophan which may also be utilized forinsomnia. Computing device 104 may eliminate 5-hydroxytryptophan as apossibly identified tolerant longevity element 180 due to thecontraindication if a user were to consume both melatonin and5-hydroxytryptophan causing excessive drowsiness, fatigue, and evenrespiratory depression. Identifying a tolerant longevity element 180 mayinclude identifying by computing device 104 a first tolerant longevityelement 180 containing a first active ingredient. Computing device 104identifies a second tolerant longevity element 180 containing a secondactive ingredient. Computing device 104 determines that the first activeingredient relates to the second active ingredient and eliminates thesecond tolerant longevity element 180. For instance and withoutlimitation, computing device 104 may identify a first tolerant longevityelement 180 such as a multi-vitamin which may contain multiple firstactive ingredients such as Vitamin A, Vitamin C, iron, zinc, and copper.Computing device 104 may identify a second tolerant longevity element180 such as an iron supplement, whereby computing device 104 maydetermine that iron contained within the second tolerant longevityelement 180 relates to the iron contained within the first tolerantlongevity element 180. In such an instance, computing device 104 mayeliminate the second tolerant longevity element 180 upon determiningthat the dose of iron contained in the second tolerant longevity element180 would be in excess of the daily recommended dose of iron whenconsumed in combination with the first tolerant longevity element 180.In an embodiment, a health professional such as an informed advisor mayoverride this decision when they may deem that both products can besafely taken together such as for example if a user recently experienceda large volume of blood loss or was in a traumatic motor vehicleaccident and lost excess amounts of blood.

Referring now to FIG. 8 , an exemplary embodiment of a method 800 ofidentifying a longevity element to optimize supplement decisions isillustrated. At step 805, a computing device captures an identifier of afirst longevity element using a data capturing device. Data capturingdevice may include, without limitation, an imaging device as describedabove in reference to FIGS. 1-7 . Examples of data capturing devicesinclude, without limitation, a camera, bar code readers, scanners, radiofrequency readers or interrogators, transceivers, or the like. Forinstance, and without limitation, computing device may receive, atransmission, such as a wireless transmission and/or a transmission overa network, from the data capturing device containing an identifierdescribing first longevity element and/or a product containing firstlongevity element, such as without limitation a photograph or an imagecontaining the first longevity device or the like. Data capturing devicemay be connected wirelessly to computing device and/or to any of aplurality of remote devices, for example, using Bluetooth® technology,infrared technology, a Wi-Fi network, or the like. Data capturing devicemay be connected to computing device and/or to a plurality of remotedevices by a wired connection over a network which may include the useof a router, a switch, a bridge, or the like. An optical code mayinclude a universal product code (“UPC”), a stock keeping code (“SKU”),a quick read code (“QR”), a European article number (“EAN”) Stillreferring to FIG. 8 , computing device may be further configured tocapture an image using the data capturing device. For instance, andwithout limitation, a computing device may receive a transmission, suchas a wireless transmission and/or a transmission over a network of animage identifying the first longevity device. In an embodiment, thecomputing device may be further configured to capture an imagecontaining the first longevity device. A computing device may be furtherconfigured to capture the identifier by scanning an optical code usingthe data capturing device. An optical code may include a bar code,and/or quick read (QR) code, and/or an universal product code (UPC),and/or an European article number (EAN), and/or an identifier selecteduser interaction and/or network communication, such as a uniformresource locator (URL), uniform resource identifier (URI), stock-keepingunit (SKU) or the like. For instance, and without limitations, acomputing device may receive a transmission, such as a wirelesstransmission and/or a transmission over a network of a barcode scannedusing the data capturing device identifying the first longevity device.In an embodiment, the computing device may be further configured tocapture the identifier by scanning an optical code using the datacapturing device. A computing device may be further configured tocapture the identifier by interrogating a radio frequency identificationtag. For example, these identifiers may originate from radio frequencyidentification (“RFID”) tags or Near Field communication (“NFC”) tags.RFID tags may include passive tags, active tags, or the like. Forinstance, and without any limitations, a computing device may receive atransmission, such as a wireless transmission and/or a transmissionafter interrogation by the data capturing device. In an embodiment,computing device may be configured to capture the identifier byinterrogating a radio frequency identification tag.

Alternatively or additionally, and still referring to FIG. 8 , longevityinquiry may be received by way of receipt of a user biologicalextraction; for instance, user may submit a new biological extractionand/or refer to a stored biological extraction while inquiring aboutlongevity elements to aid user in overcoming or preventing chronicconditions and/or extending life-span. In an embodiment, computingdevice may receive dietary training data wherein dietary training dataincludes a plurality of biological extractions and a plurality ofcorrelated longevity elements, and generate therewith a first machinelearning algorithm a dietary model relating biological extractions tolongevity elements; computing device may train first machine learningalgorithm using training data. Computing device may receive a secondbiological extraction; second biological extraction may be the samebiological extraction as a first biological extraction as describedbelow, and/or may be a different biological extraction. Receipt ofsecond biological extraction may be effected in any manner describedabove in reference to FIGS. 1-7 . Computing device may output alongevity element using the first machine learning algorithm and thesecond biological extraction; this may be accomplished withoutlimitation as described above in reference to FIGS. 1-7 . As anon-limiting example, first machine learning algorithm may include asupervised machine-learning algorithm. From the identifier of a firstlongevity element, the computing device generates a longevity inquiry.

At step 810, and further referring to FIG. 8 , the computing devicegenerates a longevity inquiry from the identifier of a first longevityelement, where the query identifies the first longevity element; thismay be implemented, without limitation, as described above in referenceto FIGS. 1-7 .

At step 815, and further referring to FIG. 8 , computing deviceretrieves a first biological extraction from a user database; this maybe implemented, without limitation, as described above in reference toFIGS. 1-7 . As a function of the first biological extraction, computingdevice selects an ADME model.

At step 820, computing device selects an ADME model as a function of thefirst biological extraction; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-7 . For instance,and without limitation, selecting an ADME model may include identifyinga genetic marker contained within a third biological extraction,generating using genetic training data including a plurality of geneticmarkers and a plurality of correlated ADME models, and using aclassification algorithm, a genetic classifier, wherein the geneticclassifier inputs a genetic marker and outputs an ADME model, andselecting an ADME model as a function of generating the geneticclassifier, for instance as described above in reference to FIGS. 1-7 .As a further non-limiting example, selecting the ADME model may includeretrieving a fourth biological extraction from the user database, wherethe fourth biological extraction includes a genetic marker containing anADME marker, and locating an ADME model containing the ADME marker, forinstance as described above in reference to FIGS. 1-7 . Utilizingselected ADME model, computing device generates a machine-learningalgorithm that inputs first longevity element associated and outputs anADME factor.

At step 825, computing device generates a machine-learning algorithmutilizing selected ADME model that inputs first longevity elementassociated and outputs an ADME factor; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-7 . As a functionof the ADME factor, the computing device determines that first longevityelement is an intolerant longevity element.

At step 830, computing device determines, as a function of the ADMEfactor, that first longevity element is a tolerant longevity element;this may be implemented, without limitation, as described above inreference to FIGS. 1-7 .

With continued reference to FIG. 8 , identifying a longevity element 132associated with a user may include generating a machine-learningalgorithm 176. Computing device 104 may receive dietary training data140. Dietary training data 140 may include any of the dietary trainingdata 140 as described above in reference to FIG. 1 . Dietary trainingdata 140 may include a plurality of data entries including biologicalextraction 124 correlated to longevity element 132. Computing device 104may receive dietary training data 140 from model database 156. Computingdevice 104 generates using a first machine-learning algorithm 176 adietary model 144 correlating biological extraction 124 with longevityelement 132. Dietary model 144 includes any of the dietary model 144 asdescribed above. Dietary model 144 may include one or more equations.Dietary model 144 may include any machine-learning process and mayinclude linear or polynomial regression algorithms. Dietary model 144may include one or more supervised machine-learning algorithm 176.Dietary model 144 may include one or more unsupervised machine-learningalgorithm 176. Dietary model 144 may include one or more lazy learningmodels. Dietary model 144 may include a set of instructions utilized togenerate outputs based on inputs derived using one or moremachine-learning algorithm 176. Computing device 104 receives a userbiological extraction 124 and outputs a longevity element 132 using afirst machine-learning algorithm 176. Longevity element 132 includes anyof the longevity element 132 as described herein.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 9 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 900 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 900 includes a processor 904 and a memory908 that communicate with each other, and with other components, via abus 912. Bus 912 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Memory 908 may include various components (e.g., machine-readable media)including, but not limited to, a random access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 916 (BIOS), including basic routines that help totransfer information between elements within computer system 900, suchas during start-up, may be stored in memory 908. Memory 908 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 920 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 908 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 900 may also include a storage device 924. Examples of astorage device (e.g., storage device 924) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 924 may be connected to bus 912 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 924 (or one or morecomponents thereof) may be removably interfaced with computer system 900(e.g., via an external port connector (not shown)). Particularly,storage device 924 and an associated machine-readable medium 928 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 900. In one example, software 920 may reside, completelyor partially, within machine-readable medium 928. In another example,software 920 may reside, completely or partially, within processor 904.

Computer system 900 may also include an input device 932. In oneexample, a user of computer system 900 may enter commands and/or otherinformation into computer system 900 via input device 932. Examples ofan input device 932 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 932may be interfaced to bus 912 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 912, and any combinations thereof. Input device 932 mayinclude a touch screen interface that may be a part of or separate fromdisplay 836, discussed further below. Input device 932 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 900 via storage device 924 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 940. A network interfacedevice, such as network interface device 940, may be utilized forconnecting computer system 900 to one or more of a variety of networks,such as network 944, and one or more remote devices 948 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 944,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 920,etc.) may be communicated to and/or from computer system 900 via networkinterface device 940.

Computer system 900 may further include a video display adapter 952 forcommunicating a displayable image to a display device, such as displaydevice 936. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 952 and display device 936 may be utilized incombination with processor 904 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 900 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 912 via a peripheral interface 956. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for identifying a longevity element tooptimize supplement decisions, the system comprising a computing device,the computing device configured to: capture an identifier of a firstlongevity element using a data capturing device; generate a longevityinquiry from the identifier, wherein the generating the longevityinquiry further comprises: extracting one or more words from thelongevity inquiry utilizing a language processing module; representingthe one or more words with one or more vectors, respectively; andidentifying the first longevity element utilizing the languageprocessing module as a function of a degree of similarity between theone or more vectors representing the one or more words; retrieve a firstbiological extraction from a user database; select an ADME (Absorption,Distribution, Metabolism, and Excretion) model as a function of thefirst biological extraction; generate a machine-learning algorithmutilizing the selected ADME model that inputs the first longevityelement and outputs an ADME factor by generating a genetic classifierusing genetic training data including a plurality of genetic markerscorrelated to a plurality of ADME models and a classification algorithm,wherein the genetic classifier inputs a genetic marker included in thefirst biological extraction and outputs an ADME factor, wherein the ADMEfactor describes the absorption, distribution, metabolism and/orexcretion of one or more longevity elements; and selecting the ADMEfactor as a function of generating the genetic classifier; and identify,as a function of the ADME factor, a tolerant longevity element, whereinthe identifying of the tolerant longevity element further comprises:identifying a second longevity element contraindicated with theidentified tolerant longevity element; and eliminating the secondlongevity element as a tolerant longevity element.
 2. The system ofclaim 1, wherein the computing device is configured to capture an imageidentifying the first longevity element using the data capturing device.3. The system of claim 1, wherein the computing device is configured tocapture the identifier by scanning an optical code using the datacapturing device.
 4. The system of claim 1, wherein the computing deviceis configured to capture the identifier by interrogating a radiofrequency identification tag.
 5. The system of claim 1, whereinreceiving the longevity inquiry further comprises receiving at an imagedevice located on the computing device a wireless transmission from aremote device containing a photograph of a longevity element.
 6. Thesystem of claim 1, wherein generating the longevity inquiry furthercomprises: receiving dietary training data wherein dietary training dataincludes a plurality of biological extractions and a plurality ofcorrelated longevity elements; generating using a first machine learningalgorithm a dietary model relating biological extractions to longevityelements; receiving a second biological extraction; and outputting thelongevity inquiry using the first machine learning algorithm based onthe second biological extraction.
 7. The system of claim 6, wherein thefirst machine learning algorithm further comprises a supervisedmachine-learning algorithm.
 8. The system of claim 1, wherein selectingan ADME model further comprises: identifying a genetic marker containedwithin a third biological extraction; generating using genetic trainingdata including a plurality of genetic markers and a plurality ofcorrelated ADME models, and using a classification algorithm, a geneticclassifier, wherein the genetic classifier inputs a genetic marker andoutputs an ADME model; and selecting an ADME model as a function ofgenerating the genetic classifier.
 9. The system of claim 1, whereinselecting the ADME model further comprises: retrieving a fourthbiological extraction from the user database wherein the fourthbiological extraction further comprises a genetic marker containing anADME marker; and locating an ADME model containing the ADME marker. 10.The system of claim 1, wherein the computing device is furtherconfigured to identify a second tolerant longevity element by selectingthe tolerant longevity element as compatible with the ADME factor.
 11. Amethod of identifying a longevity element to optimize supplementdecisions, the method comprising: capturing an identifier of a firstlongevity element using a data capturing device; generating a longevityinquiry from the identifier, wherein the generating the longevityinquiry further comprises: extracting one or more words from thelongevity inquiry utilizing a language processing module; representingthe one or more words with one or more vectors, respectively; andidentifying the first longevity element utilizing the languageprocessing module as a function of a degree of similarity between theone or more vectors representing the one or more words; retrieving afirst biological extraction from a user database; selecting an ADME(Absorption, Distribution, Metabolism, and Excretion) model as afunction of the first biological extraction; generating amachine-learning algorithm utilizing the selected ADME model that inputsthe first longevity element and outputs an ADME factor by generating agenetic classifier using genetic training data including a plurality ofgenetic markers correlated to a plurality of ADME models and aclassification algorithm, wherein the genetic classifier inputs agenetic marker included in the first biological extraction and outputsan ADME factor, wherein the ADME factor describes the absorption,distribution, metabolism and/or excretion of one or more longevityelements; and selecting the ADME factor as a function of generating thegenetic classifier; and identifying, as a function of the ADME factor, atolerant longevity element, wherein the identifying of the tolerantlongevity element further comprises: identifying a second longevityelement contraindicated with the identified tolerant longevity element;and eliminating the second longevity element as a tolerant longevityelement.
 12. The method of claim 11, further comprising configuring thecomputing device to capture an image identifying the first longevitydevice using the data capturing device.
 13. The method of claim 11,further comprising configuring the computing device to capture theidentifier by scanning an optical code using the data capturing device.14. The method of claim 11, further comprising configuring the computingdevice to capture the identifier by interrogating a radio frequencyidentification tag.
 15. The method of claim 11, wherein identifying thelongevity element associated with the user further comprises: receivingdietary training data wherein dietary training data includes a pluralityof biological extractions and a plurality of correlated longevityelements; generating using a first machine learning algorithm a dietarymodel relating biological extractions to longevity elements; receiving asecond biological extraction; and outputting the longevity inquiry usingthe first machine learning algorithm based on the second biologicalextraction.
 16. The method of claim 15, wherein generating using thefirst machine learning algorithm further comprises generating asupervised machine-learning algorithm.
 17. The method of claim 11,wherein selecting the ADME model further comprises: identifying agenetic marker contained within a third biological extraction;generating using genetic training data including a plurality of geneticmarkers and a plurality of correlated ADME models, and using aclassification algorithm, a genetic classifier, wherein the geneticclassifier inputs a genetic marker and outputs an ADME model; andelecting an ADME model as a function of generating the geneticclassifier.
 18. The method of claim 11, wherein selecting the ADME modelfurther comprises: retrieving a fourth biological extraction from theuser database wherein the biological extraction further comprises agenetic marker containing an ADME marker; and locating an ADME modelcontaining the ADME marker.
 19. The method of claim 11, whereinidentifying the tolerant longevity element further comprises:identifying a first tolerant longevity element containing a first activeingredient; identifying a second tolerant longevity element containing asecond active ingredient; determining that the first active ingredientrelates to the second active ingredient; and eliminating the secondtolerant longevity element.